Viry a Červi

Company announces intent following Ticketmaster, Santander break-ins

A month after incident response giant Mandiant suggested the litany of data thefts linked to Snowflake account intrusions had the common component of lacking multi-factor authentication (MFA) controls, the cloud storage and data analytics company is offering a mandatory MFA option to admins.…

IT giant says data exfiltration was extremely difficult to detect

Fujitsu Japan says an unspecified "advanced" malware strain was to blame for a March data theft, insisting the strain was "not ransomware", yet it hasn't revealed how many individuals are affected.…

BlackByte, LockBit among the criminals using bespoke tools

As ransomware crews increasingly shift beyond just encrypting victims' files and demanding a payment to unlock them, instead swiping sensitive info straight away, some of the more mature crime organizations are developing custom malware for their data theft.…

Fixes have been made, it appears, but disclosure or discussion is invisible

Column  Found a bug? It turns out that reporting it with a story in The Register works remarkably well ... mostly. After publication of my "Kryptonite" article about a prompt that crashes many AI chatbots, I began to get a steady stream of emails from readers – many times the total of all reader emails I'd received in the previous decade.…

Freeware AutoIt also used to hide entire PowerShell environments in scripts

A rapidly-changing infostealer malware known as ViperSoftX has evolved to become more dangerous, according to security researchers at threat detection vendor Trellix.…

If someone can do a little MITM'ing and hash cracking, they can log in with no valid password needed

Cybersecurity experts at universities and Big Tech have disclosed a vulnerability in a common client-server networking protocol that allows snoops to potentially bypass user authentication via man-in-the-middle (MITM) attacks.…

Citrix, SAP also deserve your attention – because miscreants are already thinking about Exploit Wednesday

Patch Tuesday  Clear your Microsoft system administrator's diary: The bundle of fixes in Redmond's July Patch Tuesday is a doozy, with at least two bugs under active exploitation.…

RT News snarks back after it's accused of building social nyet-work for Kremlin

The FBI and cybersecurity agencies in Canada and the Netherlands say they have taken down an almost 1,000-strong Twitter bot farm set up by Russian state-run RT News that used generative AI to spread disinformation to Americans and others.…

Understanding the power needs of the UK begins with knowing when renewals are due

Certificate Watch  Demonstrating that Microsoft is not alone in its inability to keep track of certificates is UK power market biz Elexon.…

Making cyberattack among the largest ever recorded in finance industry

Evolve Bank & Trust says the data of more than 7.6 million customers was stolen during the LockBit break-in in late May, per a fresh filing with Maine's attorney general.…

Detection is a traditional type of cybersecurity control, along with blocking, adjustment, administrative and other controls. Whereas before 2015 teams asked themselves what it was that they were supposed to detect, as MITRE ATT&CK evolved, SOCs were presented with practically unlimited space for ideas on creating detection scenarios.

With the number of scenarios becoming virtually unlimited, another question inevitably arises: “What do we detect first?” This and the fact that SOC teams forever play the long game, having to respond with limited resources to a changing threat landscape, evolving technology and increasingly sophisticated malicious actors, makes managing efforts to develop detection logic an integral part of any modern SOC’s activities.

The problem at hand is easy to put into practical terms: the bulk of the work done by any modern SOC – with the exception of certain specialized SOC types – is detecting, and responding to, information security incidents. Detection is directly associated with preparation of certain algorithms, such as signatures, hard-coded logic, statistical anomalies, machine learning and others, that help to automate the process. The preparation consists of at least two processes: managing detection scenarios and developing detection logic. These cover the life cycle, stages of development, testing methods, go-live, standardization, and so on. These processes, like any others, require certain inputs: an idea that describes the expected outcome at least in abstract terms.

This is where the first challenges arise: thanks to MITRE ATT&CK, there are too many ideas. The number of described techniques currently exceeds 200, and most are broken down into several sub-techniques – MITRE T1098 Account Manipulation, for one, contains six sub-techniques – while SOC’s resources are limited. Besides, SOC teams likely do not have access to every possible source of data for generating detection logic, and some of those they do have access to are not integrated with the SIEM system. Some sources can help with generating only very narrowly specialized detection logic, whereas others can be used to cover most of the MITRE ATT&CK matrix. Finally, certain cases require activating extra audit settings or adding selective anti-spam filtering. Besides, not all techniques are the same: some are used in most attacks, whereas others are fairly unique and will never be seen by a particular SOC team. Thus, setting priorities is both about defining a subset of techniques that can be detected with available data and about ranking the techniques within that subset to arrive at an optimized list of detection scenarios that enables detection control considering available resources and in the original spirit of MITRE ATT&CK: discovering only some of the malicious actor’s atomic actions is enough for detecting the attack.

A slight detour. Before proceeding to specific prioritization techniques, it is worth mentioning that this article looks at options based on tools built around the MITRE ATT&CK matrix. It assesses threat relevance in general, not in relation to specific organizations or business processes. Recommendations in this article can be used as a starting point for prioritizing detection scenarios. A more mature approach must include an assessment of a landscape that consists of security threats relevant to your particular organization, an allowance for your own threat model, an up-to-date risk register, and automation and manual development capabilities. All of this requires an in-depth review, as well as liaison between various processes and roles inside your SOC. We offer more detailed maturity recommendations as part of our SOC consulting services.

MITRE Data Sources

Optimized prioritization of the backlog as it applies to the current status of monitoring can be broken down into the following stages:

• Defining available data sources and how well they are connected;
• Identifying relevant MITRE ATT&CK techniques and sub-techniques;
• Finding an optimal relation between source status and technique relevance;
• Setting priorities.

A key consideration in implementing this sequence of steps is the possibility of linking information that the SOC receives from data sources to a specific technique that can be detected with that information. In 2021, MITRE completed its ATT&CK Data Sources project, its result being a methodology for describing a data object that can be used for detecting a specific technique. The key elements for describing data objects are:

• Data Source: an easily recognizable name that defines the data object (Active Directory, application log, driver, file, process and so on);
• Data Components: possible data object actions, statuses and parameters. For example, for a file data object, data components are file created, file deleted, file modified, file accessed, file metadata, and so on.

MITRE Data Sources

Virtually every technique in the MITRE ATT&CK matrix currently contains a Detection section that lists data objects and relevant data components that can be used for creating detection logic. A total of 41 data objects have been defined at the time of publishing this article.

MITRE most relevant data components

The column on the far right in the image above (Event Logs) illustrates the possibilities of expanding the methodology to cover specific events received from real data sources. Creating a mapping like this is not one of the ATT&CK Data Sources project goals. This Event Logs example is rather intended as an illustration. On the whole, each specific SOC is expected to independently define a list of events relevant to its sources, a fairly time-consuming task.

To optimize your approach to prioritization, you can start by isolating the most frequent data components that feature in most MITRE ATT&CK techniques.

The graph below presents the up-to-date top 10 data components for MITRE ATT&CK matrix version 15.1, the latest at the time of writing this.

The most relevant data components (download)

For these data components, you can define custom sources for the most results. The following will be of help:

• Expert knowledge and overall logic. Data objects and data components are typically informative enough for the engineer or analyst working with data sources to form an initial judgment on the specific sources that can be used.
• Validation directly inside the event collection system. The engineer or analyst can review available sources and match events with data objects and data components.
• Publicly available resources on the internet, such as Sensor Mappings to ATT&CK, a project by the Center for Threat-Informed Defense, or this excellent resource on Windows events: UltimateWindowsSecurity.

That said, most sources are fairly generic and typically connected when a monitoring system is implemented. In other words, the mapping can be reduced to selecting those sources which are connected in the corporate infrastructure or easy to connect.

The result is an unranked list of integrated data sources that can be used for developing detection logic, such as:

• For Command Execution: OS logs, EDR, networked device administration logs and so on;
• For Process Creation: OS logs, EDR;
• For Network Traffic Content: WAF, proxy, DNS, VPN and so on;
• For File Modification: DLP, EDR, OS logs and so on.

However, this list is not sufficient for prioritization. You also need to consider other criteria, such as:

• The quality of source integration. Two identical data sources may be integrated with the infrastructure differently, with different logging settings, one source being located only in one network segment, and so on.
• Usefulness of MITRE ATT&CK techniques. Not all techniques are equally useful in terms of optimization. Some techniques are more specialized and aimed at detecting rare attacker actions.
• Detection of the same techniques with several different data sources (simultaneously). The more options for detecting a technique have been configured, the higher the likelihood that it will be discovered.
• Data component variability. A selected data source may be useful for detecting not only those techniques associated with the top 10 data components but others as well. For example, an OS log can be used for detecting both Process Creation components and User Account Authentication components, a type not mentioned on the graph.

Prioritizing with DeTT&CT and ATT&CK Navigator

Now that we have an initial list of data sources available for creating detection logic, we can proceed to scoring and prioritization. You can automate some of this work with the help of DeTT&CT, a tool created by developers unaffiliated with MITRE to help SOCs with using MITRE ATT&CK for scoring and comparing the quality of data sources, coverage and detection scope according to MITRE ATT&CK techniques. The tool is available under the GPL-3.0 license.

DETT&CT supports an expanded list of data sources as compared to the MITRE model. This list is implemented by design and you do not need to redefine the MITRE matrix itself. The expanded model includes several data components, which are parts of MITRE’s Network Traffic component, such as Web, Email, Internal DNS, and DHCP.

You can install DETT&CT with the help of two commands: git clone and pip install -r. This gives you access to DETT&CT Editor: a web interface for describing data sources, and DETT&CT CLI for automated analysis of prepared input data that can help with prioritizing detection logic and more.

The first step in identifying relevant data sources is describing these. Go to Data Sources in DETT&CT Editor, click New file and fill out the fields:

• Domain: the version of the MITRE ATT&CK matrix to use (enterprise, mobile or ICS).
• This field is not used in analytics; it is intended for distinguishing between files with the description of sources.
• Systems: selection of platforms that any given data source belongs to. This helps to both separate platforms, such as Windows and Linux, and specify several platforms within one system. Going forward, keep in mind that a data source is assigned to a system, not a platform. In other words, if a source collects data from both Windows and Linux, you can leave one system with two platforms, but if one source collects data from Windows only, and another, from Linux only, you need to create two systems: one for Windows and one for Linux.

After filling out the general sections, you can proceed to analyzing data sources and mapping to the MITRE Data Sources. Click Add Data Source for each MITRE data object and fill out the relevant fields. Follow the link above for a detailed description of all fields and example content on the project page. We will focus on the most interesting field: Data quality. It describes the quality of data source integration as determined according to five criteria:

• Device completeness. Defines infrastructure coverage by the source, such as various versions of Windows or subnet segments, and so on.
• Data field completeness. Defines the completeness of data in events from the source. For example, information about Process Creation may be considered incomplete if we see that a process was created, but not the details of the parent process, or for Command Execution, we see the command but not the arguments, and so on.
• Defines the presence of a delay between the event happening and being added to a SIEM system or another detection system.
• Defines the extent to which the names of the data fields in an event from this source are consistent with standard naming.
• Compares the period for which data from the source is available for detection with the data retention policy defined for the source. For instance, data from a certain source is available for one month, whereas the policy or regulatory requirements define the retention period as one year.

A detailed description of the scoring system for filling out this field is available in the project description.

It is worth mentioning that at this step, you can describe more than just the top 10 data components that cover the majority of the MITRE ATT&CK techniques. Some sources can provide extra information: in addition to Process Creation, Windows Security Event Log provides data for User Account Authentication. This extension will help to analyze the matrix without limitations in the future.

After describing all the sources on the list defined earlier, you can proceed to analyze these with reference to the MITRE ATT&CK matrix.

The first and most trivial analytical report identifies the MITRE ATT&CK techniques that can be discovered with available data sources one way or another. This report is generated with the help of a configuration file with a description of data sources and DETT&CT CLI, which outputs a JSON file with MITRE ATT&CK technique coverage. You can use the following command for this:

python dettect.py ds -fd <data-source-yaml-dir>/<data-sources-file.yaml> -l

The resulting JSON is ready to be used with the MITRE ATT&CK matrix visualization tool, MITRE ATT&CK Navigator. See below for an example.

MITRE ATT&CK coverage with available data sources

This gives a literal answer to the question of what techniques the SOC can discover with the set of data sources that it has. The numbers in the bottom right-hand corner of some of the cells reflect sub-technique coverage by the data sources, and the colors, how many different sources can be used to detect the technique. The darker the color, the greater the number of sources.

DETT&CT CLI can also generate an XLSX file that you can conveniently use as the integration of existing sources evolves, a parallel task that is part of the data source management process. You can use the following command to generate the file:

python dettect.py ds -fd <data-source-yaml-dir>/<data-sources-file.yaml> -e

The next analytical report we are interested in assesses the SOC’s capabilities in terms of detecting MITRE ATT&CK techniques and sub-techniques while considering the scoring of integrated source quality as done previously. You can generate the report by running the following command:

python dettect.py ds -fd <data-source-yaml-dir>/<data-sources-file.yaml> --yaml

This generates a DETT&CT configuration file that both contains matrix coverage information and considers the quality of the data sources, providing a deeper insight into the level of visibility for each technique. The report can help to identify the techniques for which the SOC in its current shape can achieve the best results in terms of completeness of detection and coverage of the infrastructure.

This information too can be visualized with MITRE ATT&CK Navigator. You can use the following DETT&CT CLI command for this:

python dettect.py v -ft output/<techniques-administration-file.yaml> -l

See below for an example.

MITRE ATT&CK coverage with available sources considering their quality

For each technique, the score is calculated as an average of all relevant data source scores. For each data source, it is calculated from specific parameters. The following parameters have increased weight:

• Device completeness;
• Data field completeness;
• Retention.

To set up the scoring model, you need to modify the project source code.

It is worth mentioning that the scoring system presented by the developers of DETT&CT tends to be fairly subjective in some cases, for example:

• You may have one data source out of the three mentioned in connection with the specific technique. However, in some cases, one data source may not be enough even to detect the technique on a minimal level.
• In other cases, the reverse may be true, with one data source giving exhaustive information for complete detection of the technique.
• Detection may be based on a data source that is not currently mentioned in the MITRE ATT&CK Data Sources or Detections for that particular technique.

In these cases, the DETT&CT configuration file techniques-administration-file.yaml can be adjusted manually.

Now that the available data sources and the quality of their integration have been associated with the MITRE ATT&CK matrix, the last step is ranking the available techniques. You can use the Procedure Examples section in the matrix, which defines the groups that use a specific technique or sub-technique in their attacks. You can use the following DETT&CT command to run the operation for the entire MITRE ATT&CK matrix:

python dettect.py g

In the interests of prioritization, we can merge the two datasets (technique feasibility considering available data sources and their quality, and the most frequently used MITRE ATT&CK techniques):

python dettect.py g -p PLATFORM -o output/<techniques-administration- file.yaml> -t visibility

The result is a JSON file containing techniques that the SOC can work with and their description, which includes the following:

• Detection ability scoring;
• Known attack frequency scoring.

See the image below for an example.

Technique frequency and detection ability

As you can see in the image, some of the techniques are colored shades of red, which means they have been used in attacks (according to MITRE), but the SOC has no ability to detect them. Other techniques are colored shades of blue, which means the SOC can detect them, but MITRE has no data on these techniques having been used in any attacks. Finally, the techniques colored shades of orange are those which groups known to MITRE have used and the SOC has the ability to detect.

It is worth mentioning that groups, attacks and software used in attacks, which are linked to a specific technique, represent retrospective data collected throughout the period that the matrix has existed. In some cases, this may result in increased priority for techniques that were relevant for attacks, say, from 2015 through 2020, which is not really relevant for 2024.

However, isolating a subset of techniques ever used in attacks produces more meaningful results than simple enumeration. You can further rank the resulting subset in the following ways:

• By using the MITRE ATT&CK matrix in the form of an Excel table. Each object (Software, Campaigns, Groups) contains the property Created (date when the object was created) that you can rely on when isolating the most relevant objects and then use the resulting list of relevant objects to generate an overlap as described above:
  python dettect.py g -g sample-data/groups.yaml -p PLATFORM -o output/<techniques-administration-file.yaml> -t visibility
• By using the TOP ATT&CK TECHNIQUES project created by MITRE Engenuity.

TOP ATT&CK TECHNIQUES was aimed at developing a tool for ranking MITRE ATT&CK techniques and accepts similar inputs to DETT&CT. The tool produces a definition of 10 most relevant MITRE ATT&CK techniques for detecting with available monitoring capabilities in various areas of the corporate infrastructure: network communications, processes, the file system, cloud-based solutions and hardware. The project also considers the following criteria:

• Choke Points, or specialized techniques where other techniques converge or diverge. Examples of these include T1047 WMI, as it helps to implement a number of other WMI techniques, or T1059 Command and Scripting Interpreter, as many other techniques rely on a command-line interface or other shells, such as PowerShell, Bash and others. Detecting this technique will likely lead to discovering a broad spectrum of attacks.
• Prevalence: technique frequency over time.

MITRE ATT&CK technique ranking methodology in TOP ATT&CK TECHNIQUES

Note, however, that the project is based on MITRE ATT&CK v.10 and is not supported.

Finalizing priorities

By completing the steps above, the SOC team obtains a subset of MITRE ATT&CK techniques that feature to this or that extent in known attacks and can be detected with available data sources, with an allowance for the way these are configured in the infrastructure. Unfortunately, DETT&CT does not offer any way of creating a convenient XLSX file with an overlap between techniques used in attacks and those that the SOC can detect. However, we have a JSON file that can be used to generate the overlap with the help of MITRE ATT&CK Navigator. So, all you need to do for prioritization is to parse the JSON, say, with the help of Python. The final prioritization conditions may be as follows:

• Priority 1 (critical): Visibility_score >= 3 and Attacker_score >= 75. From an applied perspective, this isolates MITRE ATT&CK techniques that most frequently feature in attacks and that the SOC requires minimal or no preparation to detect.
• Priority 2 (high): (Visibility_score < 3 and Visibility_score >= 1) and Attacker_score >= 75. These are MITRE ATT&CK techniques that most frequently feature in attacks and that the SOC is capable of detecting. However, some work on logging may be required, or monitoring coverage may not be good enough.
• Priority 3 (medium): Visibility_score >= 3 and Attacker_score < 75. These are MITRE ATT&CK techniques with medium to low frequency that the SOC requires minimal or no preparation to detect.
• Priority 4 (low): (Visibility_score < 3 and Visibility_score >= 1) and Attacker_score < 75. These are all other MITRE ATT&CK techniques that feature in attacks and the SOC has the capability to detect.

As a result, the SOC obtains a list of MITRE ATT&CK techniques ranked into four groups and mapped to its capabilities and global statistics on malicious actors’ actions in attacks. The list is optimized in terms of the cost to write detection logic and can be used as a prioritized development backlog.

Prioritization extension and parallel tasks

In conclusion, we would like to highlight the key assumptions and recommendations for using the suggested prioritization method.

• As mentioned above, it is not fully appropriate to use the MITRE ATT&CK statistics on the frequency of techniques in attacks. For more mature prioritization, the SOC team must rely on relevant threat data. This requires defining a threat landscape based on analysis of threat data, mapping applicable threats to specific devices and systems, and isolating the most relevant techniques that may be used against a specific system in the specific corporate environment. An approach like this calls for in-depth analysis of all SOC activities and links between processes. Thus, when generating a scenario library for a customer as part of our consulting services, we leverage Kaspersky Threat Intelligence data on threats relevant to the organization, Managed Detection and Response statistics on detected incidents, and information about techniques that we obtained while investigating real-life incidents and analyzing digital evidence as part of Incident Response service.
• The suggested method relies on SOC capabilities and essential MITRE ATT&CK analytics. That said, the method is optimized for effort reduction and helps to start developing relevant detection logic immediately. This makes it suitable for small-scale SOCs that consist of a SIEM administrator or analyst. In addition to this, the SOC builds what is essentially a detection functionality roadmap, which can be used for demonstrating the process, defining KPIs and justifying a need for expanding the team.

Lastly, we introduce several points regarding the possibilities for improving the approach described herein and parallel tasks that can be done with tools described in this article.

You can use the following to further improve the prioritization process.

• Grouping by detection. On a basic level, there are two groups: network detection or detection on a device. Considering the characteristics of the infrastructure and data sources in creating detection logic for different groups helps to avoid a bias and ensure a more complete coverage of the infrastructure.
• Grouping by attack stage. Detection at the stage of Initial Access requires more effort, but it leaves more time to respond than detection at the Exfiltration stage.
• Criticality coefficient. Certain techniques, such as all those associated with vulnerability exploitation or suspicious PowerShell commands, cannot be fully covered. If this is the case, the criticality level can be used as an additional criterion.
• Granular approach when describing source quality. As mentioned earlier, DETT&CT helps with creating quality descriptions of available data sources, but it lacks exception functionality. Sometimes, a source is not required for the entire infrastructure, or there is more than one data source providing information for similar systems. In that case, a more granular approach that relies on specific systems, subnets or devices can help to make the assessment more relevant. However, an approach like that calls for liaison with internal teams responsible for configuration changes and device inventory, who will have to at least provide information about the business criticality of assets.

Besides improving the prioritization method, the tools suggested can be used for completing a number of parallel tasks that help the SOC to evolve.

• Expanding the list of sources. As shown above, the coverage of the MITRE ATT&CK matrix requires diverse data sources. By mapping existing sources to techniques, you can identify missing logs and create a roadmap for connecting or introducing these sources.
• Improving the quality of sources. Scoring the quality of data sources can help create a roadmap for improving existing sources, for example in terms of infrastructure coverage, normalization or data retention.
• Detection tracking. DETT&CT offers, among other things, a detection logic scoring feature, which you can use to build a detection scenario revision process.

Fairly 'low budget', unsophisticated malware, say researchers, but it can collect the same data as Pegasus

Interview  When it comes to surveillance malware, sophisticated spyware with complex capabilities tends to hog the limelight – for example NSO Group's Pegasus, which is sold to established governments. But it's actually less polished kit that you've never heard of, like GuardZoo – developed and used by Houthi rebels in Yemen – that dominates the space.…

Google's absence creates software distribution issues not even mighty Microsoft can handle

Microsoft China will provide staff with Apple devices so they can log on to the software giant's systems.…

Scum keep databases of the people they've already skimmed

Australia's Competition and Consumer Commission has warned that scammers are targeting scam victims with fake offers to help them recover from scams.…

Lax patching and vulnerable small biz kit make life easy for Beijing's secret-stealers

Law enforcement agencies from eight nations, led by Australia, have issued an advisory that details the tradecraft used by China-aligned threat actor APT40 – aka Kryptonite Panda, GINGHAM TYPHOON, Leviathan and Bronze Mohawk – and found it prioritizes developing exploits for newly found vulnerabilities and can target them within hours.…

Injecting Copilot branding will not make TLS certificates auto-renew

Another Microsoft certificate has expired, leaving SwiftKey users that are seeking support faced with an alarming certificate error.…

Good riddance to another pesky tribe of miscreants

Updated  Researchers at Avast have provided decryptors to DoNex ransomware victims on the down-low since March after discovering a flaw in the crims' cryptography, the company confirmed today.…

How to get ready for the future of digital identity in the European Union from eIDAS 1.0 to eIDAS 2.0 and beyond

Partner Content  : Opening a bank account, making or receiving a payment, instructing an accountant or booking a doctor's appointment. These everyday tasks depend on identity, either proving who you are or verifying who you're dealing with.…

In May 2024, we discovered a new advanced persistent threat (APT) targeting Russian government entities that we dubbed CloudSorcerer. It’s a sophisticated cyberespionage tool used for stealth monitoring, data collection, and exfiltration via Microsoft Graph, Yandex Cloud, and Dropbox cloud infrastructure. The malware leverages cloud resources as its command and control (C2) servers, accessing them through APIs using authentication tokens. Additionally, CloudSorcerer uses GitHub as its initial C2 server.

CloudSorcerer’s modus operandi is reminiscent of the CloudWizard APT that we reported on in 2023. However, the malware code is completely different. We presume that CloudSorcerer is a new actor that has adopted a similar method of interacting with public cloud services.

Our findings in a nutshell:

• CloudSorcerer APT uses public cloud services as its main C2s
• The malware interacts with the C2 using special commands and decodes them using a hardcoded charcode table.
• The actor uses Microsoft COM object interfaces to perform malicious operations.
• CloudSorcerer acts as separate modules (communication module, data collection module) depending on which process it’s running, but executes from a single executable.

Technical details Initial start up MD5 f701fc79578a12513c369d4e36c57224 SHA1 f1a93d185d7cd060e63d16c50e51f4921dd43723 SHA256 e4b2d8890f0e7259ee29c7ac98a3e9a5ae71327aaac658f84072770cf8ef02de Link time N/A Compiler N/A File type Windows x64 executable File size 172kb File name N/A

The malware is executed manually by the attacker on an already infected machine. It is initially a single Portable Executable (PE) binary written in C. Its functionality varies depending on the process in which it is executed. Upon execution, the malware calls the GetModuleFileNameA function to determine the name of the process it is running in. It then compares this process name with a set of hardcoded strings: browser, mspaint.exe, and msiexec.exe. Depending on the detected process name, the malware activates different functions:

• If the process name is mspaint.exe, CloudSorcerer functions as a backdoor module, and performs activities such as data collection and code execution.
• If the process name is msiexec.exe, the CloudSorcerer malware initiates its C2 communication module.
• Lastly, if the process name contains the string “browser” or does not match any of the specified names, the malware attempts to inject shellcode into either the msiexec.exe, mspaint.exe, or explorer.exe processes before terminating the initial process.

The shellcode used by CloudSorcerer for initial process migration shows fairly standard functionality:

• Parse Process Environment Block (PEB) to identify offsets to required Windows core DLLs;
• Identify required Windows APIs by hashes using ROR14 algorithm;
• Map CloudSorcerer code into the memory of one of the targeted processes and run it in a separate thread.

All data exchange between modules is organized through Windows pipes, a mechanism for inter-process communication (IPC) that allows data to be transferred between processes.

CloudSorcerer backdoor module

The backdoor module begins by collecting various system information about the victim machine, running in a separate thread. The malware collects:

• Computer name;
• User name;
• Windows subversion information;
• System uptime.

All the collected data is stored in a specially created structure. Once the information gathering is complete, the data is written to the named pipe \\.\PIPE\[1428] connected to the C2 module process. It is important to note that all data exchange is organized using well-defined structures with different purposes, such as backdoor command structures and information gathering structures.

Next, the malware attempts to read data from the pipe \\.\PIPE\[1428]. If successful, it parses the incoming data into the COMMAND structure and reads a single byte from it, which represents a COMMAND_ID.

Main backdoor functionality

Depending on the COMMAND_ID, the malware executes one of the following actions:

• 0x1 – Collect information about hard drives in the system, including logical drive names, capacity, and free space.
• 0x2 – Collect information about files and folders, such as name, size, and type.
• 0x3 – Execute shell commands using the ShellExecuteExW API.
• 0x4 – Copy, move, rename, or delete files.
• 0x5 – Read data from any file.
• 0x6 – Create and write data to any file.
• 0x8 – Receive a shellcode from the pipe and inject it into any process by allocating memory and creating a new thread in a remote process.
• 0x9 – Receive a PE file, create a section and map it into the remote process.
• 0x7 – Run additional advanced functionality.

When the malware receives a 0x7 COMMAND_ID, it runs one of the additional tasks described below:

Command ID Operation Description 0x2307 Create process Creates any process using COM interfaces, used for running downloaded binaries. 0x2407 Create process as dedicated user Creates any process under dedicated username. 0x2507 Create process with pipe Creates any process with support of inter-process communication to exchange data with the created process. 0x3007 Clear DNS cache Clears the DNS cache. 0x2207 Delete task Deletes any Windows task using COM object interfaces. 0x1E07 Open service Opens a Windows service and reads its status. 0x1F07 Create new task Creates a new Windows task and sets up a trigger for execution using COM objects. 0x2007 Get tasks Gets the list of all the Windows tasks using COM object interface. 0x2107 Stop task Stops any task using COM object interface. 0x1D07 Get services Gets the list of all Windows services. 0x1907 Delete value from reg Deletes any value from any Windows registry key selected by the actor. 0x1A07 Create service Creates a new Windows service. 0x1B07 Change service Modifies any Windows service configuration. 0x1807 Delete reg key Deletes any Windows registry key. 0x1407 Get TCP/UDP update table Gets information from Windows TCP/UDP update table. 0x1507 Collect processes Collects all running processes. 0x1607 Set reg key value Modifies any Windows registry key. 0x1707 Enumerate reg key Enumerates Windows registry keys. 0x1307 Enumerate shares Enumerates Windows net shares. 0x1007 Set net user info Sets information about a user account on a Windows network using NetUserSetInfo. It allows administrators to modify user account properties on a local or remote machine. 0x1107 Get net members Gets a member of the local network group. 0x1207 Add member Adds a user to the local network group. 0xE07 Get net user info Collects information about a network user. 0xB07 Enumerate net users Enumerates network users. 0xC07 Add net user Adds a new network user. 0xD07 Delete user Deletes a network user. 0x907 Cancel connection Cancels an existing network connection. This function allows for the disconnection of network resources, such as shared directories. 0x507 File operations Copies, moves, or deletes any file. 0x607 Get net info Collects information about the network and interfaces. 0x707 Enumerate connections Enumerates all network connections. 0x807 Map network Maps remote network drive. 0x407 Read file Reads any file as text strings. 0x107 Enumerate RDP Enumerates all RDP sessions. 0x207 Run WMI Runs any WMI query using COM object interfaces. 0x307 Get files Creates list of files and folders.

All the collected information or results of performed tasks are added to a specially created structure and sent to the C2 module process via a named pipe.

C2 module

The C2 module starts by creating a new Windows pipe named \\.\PIPE\[1428]. Next, it configures the connection to the initial C2 server by providing the necessary arguments to a sequence of Windows API functions responsible for internet connections:

• InternetCrackUrlA;
• InternetSetOptionA;
• InternetOpenA;
• InternetConnectA;
• HttpOpenRequestA;
• HttpSendRequestA

The malware sets the request type (“GET”), configures proxy information, sets up hardcoded headers, and provides the C2 URL.

Setting up internet connection

The malware then connects to the initial C2 server, which is a GitHub page located at https://github[.]com/alinaegorovaMygit. The malware reads the entire web page into a memory buffer using the InternetReadFile call.

The GitHub repository contains forks of three public projects that have not been modified or updated. Their purpose is merely to make the GitHub page appear legitimate and active. However, the author section of the GitHub page displays an interesting string:

Hex string in the author section

We found data that looks like a hex string that starts and ends with the same byte pattern – “CDOY”. After the malware downloads the entire GitHub HTML page, it begins parsing it, searching specifically for the character sequence “CDOY”. When it finds it, it copies all the characters up to the second delimiter “CDOY” and then stores them in a memory buffer. Next, the malware parses these characters, converting them from string values to hex values. It then decodes the string using a hardcoded charcode substitution table – each byte from the parsed string acts as an index in the charcode table, pointing to a substitutable byte, thus forming a new hex byte array.

Decoding algorithm

Charcode table

Alternatively, instead of connecting to GitHub, CloudSorcerer also tries to get the same data from hxxps://my.mail[.]ru/, which is a Russian cloud-based photo hosting server. The name of the photo album contains the same hex string.

The first decoded byte of the hex string is a magic number that tells the malware which cloud service to use. For example, if the byte is “1”, the malware uses Microsoft Graph cloud; if it is “0”, the malware uses Yandex cloud. The subsequent bytes form a string of a bearer token that is used for authentication with the cloud’s API.

Depending on the magic number, the malware creates a structure and sets an offset to a virtual function table that contains a subset of functions to interact with the selected cloud service.

Different virtual tables for Yandex and Microsoft

Next, the malware connects to the cloud API by:

• Setting up the initial connection using InternetOpenA and InternetConnectA;
• Setting up all the required headers and the authorization token received from the GitHub page;
• Configuring the API paths in the request;
• Sending the request using HttpSendRequestExA and checking for response errors;
• Reading data from the cloud using InternetReadFile.

The malware then creates two separate threads – one responsible for receiving data from the Windows pipe and another responsible for sending data to it. These threads facilitate asynchronous data exchange between the C2 and backdoor modules.

Finally, the C2 module interacts with the cloud services by reading data, receiving encoded commands, decoding them using the character code table, and sending them via the named pipe to the backdoor module. Conversely, it receives the command execution results or exfiltrated data from the backdoor module and writes them to the cloud.

Infrastructure GitHub page

The GitHub page was created on May 7, 2024, and two repositories were forked into it on the same day. On May 13, 2024, another repository was forked, and no further interactions with GitHub occurred. The forked repositories were left untouched. The name of the C2 repository, “Alina Egorova,” is a common Russian female name; however, the photo on the GitHub page is of a male and was copied from a public photo bank.

Mail.ru photo hosting

This page contains the same encoded string as the GitHub page. There is no information about when the album was created and published. The photo of the owner is the same as the picture from the photo bank.

Cloud infrastructure Service Main URL Initial path Yandex Cloud cloud-api.yandex.net /v1/disk/resources?path=
/v1/disk/resources/download?path=
/v1/disk/resources/upload?path= Microsoft Graph graph.microsoft.com /v1.0/me/drive/root:/Mg/%s/%s:/content Dropbox content.dropboxapi.com /2/files/download
/2/files/upload Attribution

The use of cloud services is not new, and we reported an example of this in our overview of the CloudWizard APT (a campaign in the Ukrainian conflict with ties to Operation Groundbait and CommonMagic). However, the likelihood of attributing CloudSorcerer to the same actor is low, as the code and overall functionality of the malware are different. We therefore assume at this point that CloudSorcerer is a new actor that has adopted the technique of interacting with public cloud services.

Victims

Government organizations in the Russian Federation.

Conclusions

The CloudSorcerer malware represents a sophisticated toolset targeting Russian government entities. Its use of cloud services such as Microsoft Graph, Yandex Cloud, and Dropbox for C2 infrastructure, along with GitHub for initial C2 communications, demonstrates a well-planned approach to cyberespionage. The malware’s ability to dynamically adapt its behavior based on the process it is running in, coupled with its use of complex inter-process communication through Windows pipes, further highlights its sophistication.

While there are similarities in modus operandi to the previously reported CloudWizard APT, the significant differences in code and functionality suggest that CloudSorcerer is likely a new actor, possibly inspired by previous techniques but developing its own unique tools.

Indicators of Compromise

File Hashes (malicious documents, Trojans, emails, decoys)

F701fc79578a12513c369d4e36c57224 CloudSorcerer

Domains and IPs

hxxps://github[.]com/alinaegorovaMygit CloudSorcerer C2 hxxps://my.mail[.]ru/yandex.ru/alinaegorova2154/photo/1 CloudSorcerer C2

Yara Rules
rule apt_cloudsorcerer { meta: description = "Detects CloudSorcerer" author = "Kaspersky" copyright = "Kaspersky" distribution = "DISTRIBUTION IS FORBIDDEN. DO NOT UPLOAD TO ANY MULTISCANNER OR SHARE ON ANY THREAT INTEL PLATFORM" version = "1.0" last_modified = "2024-06-06" hash = "F701fc79578a12513c369d4e36c57224" strings: $str1 = "Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko" $str2 = "c:\\windows\\system32\\mspaint.exe" $str3 = "C:\\Windows\\system32\\msiexec.exe" $str4 = "\\\\.\\PIPE\\" condition: uint16(0) == 0x5A4D and all of ($str*) } MITRE ATT&CK Mapping Tactic Technique Technique Name Execution T1059.009 Command and Scripting Interpreter: Cloud API T1559 Inter-Process Communication T1053 Scheduled Task/Job T1047 Windows Management Instrumentation Persistence T1543 Create or Modify System Process T1053 Scheduled Task/Job Defense Evasion T1140 Deobfuscate/Decode Files or Information T1112 Modify Registry Discovery T1083 File and Directory Discovery T1046 Network Service Discovery T1057 Process Discovery T1012 Query Registry T1082 System Information Discovery Collection T1005 Data from Local System Command and Control T1102 Web Service T1568 Dynamic Resolution Exfiltration T1567 Exfiltration Over Web Service T1537 Transfer Data to Cloud Account

Vietnam now requires it for some purchases. It may be a fraud risk in Singapore. Or ML could be making it safe

The use of selfies to verify identity online is an emerging trend in some parts of the world since the pandemic forced more business to go digital. Some banks – and even governments – have begun requiring live images over Zoom or similar in order to participate in the modern economy. The question must be asked, though: is it cyber smart?…