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Indická společnost SS Innovations International před časem vyvinula platformu Mantra pro robotickou chirurgii v terénu. Nedávno dostali nápad, že ji namontují na dron. Tak vznikl Project Vimana, létající chirurg, který bude provádět první zákroky na zraněných přímo na bojišti, na místě nehody nebo třeba v dějišti katastrofy.

Klonování savců narazilo na tvrdý biologický limit. Populace, která by se rozmnožovala výhradně klonováním, by vyhynula.

Francouzská vláda oznámila, že v rámci strategie 'digitální suverenity' zahájí 'přechod od systému Windows k počítačům s operačním systémem Linux' (sa sortie de Windows au profit de postes sous système d'exploitation Linux). DINUM (meziresortní ředitelství pro digitální technologie) požádalo ministerstva, aby do podzimu 2026 vypracovaly konkrétní plány nasazení Linuxu. Francie již dříve migrovala části státní správy na otevřená řešení.

It’s a step change in cybersecurity. Exploits that would take experts weeks to develop can now be generated in hours.

Concerns about AI’s ability to turbocharge cybersecurity threats have been building for years. Anthropic’s latest model could mark a turning point after the company claimed the model could identify and exploit zero-day vulnerabilities in every major operating system and web browser.

One of the standout use cases for large language models is analyzing and writing code. This has long raised worries that the technology could help automate much of the work of hackers, potentially lowering the barrier for cyberattacks.

Leading models have demonstrated steady progress on various cybersecurity-related benchmarks, and there has been evidence malicious actors are using the technology. But so far, the impact appears to have been modest, suggesting practical barriers remain that prevent the widespread use of the technology.

According to Anthropic, that’s about to change. The company says its latest model, Mythos, has hacking capabilities so potent the company will not make it publicly available. Instead, it’s releasing Mythos to a select group of major technology companies and open source developers as part of an initiative called Project Glasswing. Those participating can use the model to identify vulnerabilities in their code and patch them before hackers get access to similar capabilities.

“The vulnerabilities that Mythos Preview finds and then exploits are the kind of findings that were previously only achievable by expert professionals,” the company’s researchers write in a blog post. “We believe the capabilities that future language models bring will ultimately require a much broader, ground-up reimagining of computer security as a field.”

Fortune first reported news of Mythos last month, after a data leak at Anthropic revealed details about the new model. While the AI excels at cybersecurity tasks, it’s designed to be a general purpose model, and the company says its hacking capabilities are simply a result of vastly improved coding and reasoning skills.

In testing, Anthropic’s researchers discovered the model was able to find “zero-day” vulnerabilities—ones that were previously undiscovered—in every major operating system and web browser. Many were decades old, an indicator of how hard they were to detect.

But the model isn’t just good at finding vulnerabilities. The company’s red team—security researchers who simulate hacking attacks to identify security weaknesses—showed the model could chain together multiple vulnerabilities to create complex attacks capable of sidestepping defenses.

Its capabilities are a step change from the previous best models. Given the challenge of attacking the Firefox web browser’s JavaScript engine, Anthropic’s previous most powerful model Opus 4.6 succeeded just twice, compared to 181 times for Mythos. Most worryingly, the team found that engineers with no security background could use it to develop successful attacks overnight.

Key to the new capabilities is the model’s ability to operate autonomously for long stretches. To find bugs, the researchers used Anthropic’s coding agent Claude Code to call the model and give it a simple prompt to scan for vulnerabilities in a particular codebase. The model then read the code, came up with hypotheses about potential bugs, and ran tests to validate them without any human involvement.

The Anthropic team says Mythos fundamentally reshapes the cybersecurity landscape as exploits that would take experts weeks to develop can now be generated in hours. In particular, they note that so-called “defense-in-depth” measures that make it time-consuming and costly to attack a system may prove ineffective against models like Mythos.

“When run at large scale, language models grind through these tedious steps quickly,” they write. “Mitigations whose security value comes primarily from friction rather than hard barriers may become considerably weaker against model-assisted adversaries.”

The head of Anthropic’s frontier red team, Logan Graham, told Axios that they expect other companies to produce models with similar capabilities in the coming six to 18 months. Sources familiar with the matter told Axios that OpenAI is already finalizing a model with similar capabilities to Mythos, which will have a similarly limited release.

In its blog post, the company’s researchers note that new security technology has historically benefited defenders more than attackers. If frontier labs are careful about model releases, they think the same could be true here too, but the transitional period is likely to be disruptive.

“We need to prepare now for a world where these capabilities are broadly available in 6, 12, 24 months,” Graham told Wired. “Many things would be different about security. Many of the assumptions that we’ve built the modern security paradigms on might break.”

Whether AI developers can keep a lid on these capabilities long enough for the rest of the world to come to grips with this new reality remains to be seen. But either way, cybersecurity is likely to be even higher up the list of priorities in most boardrooms going forward.

The post Anthropic’s Mythos AI Uncovered Serious Security Holes in Every Major OS and Browser appeared first on SingularityHub.

Po hi-endových strojích se podíváme na další dostupný notebook, tentokrát s desktopovým procesorem Intel Celeron, který je založen na geniální architektuře Pentium 4.

Macbook Neo je podle Lukáše nejdůležitějším modelem za dlouhou dobu. Ukazuje totiž, jak může vypadat dobrý, a přitom levný notebook. Americká firma se uchýlila ke kompromisům na správných místech. Neo nabízí dostatečný výkon do kanceláře, celodenní výdrž, skvělý displej a barevné šasi. Základní ...

Over the next few years, agentic AI is expected to bring not only rapid technological breakthroughs, but a societal transformation, redefining how we live, work and interact with the world. And this shift is happening quickly. “By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously,” according to research firm Gartner.

Unlike traditional AI, which typically follows preset rules or algorithms, agentic AI adapts to new situations, learns from experiences, and operates independently to pursue goals without human intervention. In short, agentic AI empowers systems to act autonomously, making decisions and executing tasks — even communicating directly with other AI agents — with little or no human involvement.

Agentic AI will enable machines to interact with the physical world with unprecedented intelligence, allowing them to perform complex tasks in dynamic environments, which could be especially useful for industries facing labor shortages or hazardous conditions.However, the rise of agentic AI also brings security and ethical concerns. Ensuring these autonomous systems operate safely, transparently and responsibly will require governance frameworks and testing.

Follow this page for ongoing agentic AI coverage from Computerworld and Foundry’s other publications.

Agentic AI news and insights AI agents aren’t failing. The coordination layer is failing

April 10, 2026: Our multi-agent AI system was impressive in demos. One agent handled customer inquiries. Another managed scheduling. A third processed documents. Each worked beautifully in isolation. In production, AI agents fought each other. 

Microsoft’s new Agent Governance Toolkit targets top OWASP risks for AI agents

April 8, 2026: Microsoft has quietly introduced the Agent Governance Toolkit, an open source project designed to monitor and control AI agents during execution as enterprises try, and move them into production workflows.

Multi-agent AI is the new microservices

April 6, 2026: Our current infatuation with multi-agent systems risks mistaking a useful pattern for an inevitable future, just as we once did with microservices. Remember those? 

Without controls, an AI agent can cost more than an employee

April 3, 2026:  Long-time tech investor Jason Calacanis noted that agent costs quickly rose to $300 a day while using the Claude API at one of his organizations. At the same time, these $100,000-a-year agents were replacing a fraction of an employee’s work.

4 agentic AI success stories

April 2, 2026: Organizations are leveraging agentic AI for everything from managing customer inquiries to automating logistics, optimizing workflows, detecting fraud, and generating and testing code.

Asana’s chief product officer: Why enterprise AI agents should be ‘multiplayer by design’

April 1, 2026: As AI agents become more embedded in workplace tools, Asana is positioning its approach around collaboration rather than individual productivity.

The one-model trap: Why agentic AI won’t scale in production

March 26, 2026: Production agents don’t fail because the model is “bad.” Agentic AI fails because the operating environment is messy: requests change shape, latency budgets conflict, tools flake out, costs spike, policy constraints shift and failure modes compound.

Oracle bets on agentic apps in Fusion suite to ‘fully’ automate business processes

March 24, 2026: Oracle is recasting its Fusion Cloud Applications suite as something that not just flags insights for humans to act on. The debut of Fusion Agentic Applications, an upgraded set of applications that embeds AI agents directly into transactional business workflows, is designed to make decisions without human intervention.

Cisco goes all in on agentic AI security

March 26, 2026: Cisco is rolling out identity and access management capabilities, a toolkit customers can use to embed security controls in AI agents, and automation features that will allow security operations teams to quickly see and respond to problems.

AI agents still need humans to teach them

February 20, 2026: AI agents need skills — specific procedural knowledge — to perform tasks well, but they can’t teach themselves, a new research suggests.

Why most agentic AI projects stall before they scale

February 18, 2026: As enterprises race from pilots to autonomous systems, rising costs, fragile governance, and unrealistic expectations are forcing a reckoning. So what separates agentic AI initiatives that survive from those that quietly shut down?

How agentic AI helps prospective and existing students at DeVry

February 18, 2026: DeVry is no stranger to AI. It’s used the technology in its classrooms for 10 years and started experimenting with NLP bots and gen AI use cases for internal use as soon as it became widely available. So in April 2025, Devry University deployed its first AI agent.

Task management software gets an agentic boost

February 11, 2026: Task management apps aren’t just for storing and tracking data — they act on it. Explore tools that tap AI to auto-generate workflows, balance team capacity, and eliminate administrative overhead.

OpenClaw: The AI agent that’s got humans taking orders from bots

February 6, 2026: How one man’s vibe-coding session evolved into a reckless global AI experiment where nobody’s accountable.

Forward Networks launches agentic AI system built on network digital twin

January 30, 2026: The new Forward AI capability builds on the vendor’s digital twin and is designed to allow network teams to ask complex questions, understand network behavior, validate outcomes and safely automate workflows.

Agentic AI exposes what we’re doing wrong

January 23, 2026: Agentic AI has changed cloud computing, but not in the way the hype machine wants you to believe. It hasn’t magically replaced engineering, nor has it made architecture irrelevant. 

How to get your enterprise architecture ready for agentic AI

January 22, 2026: While C-suite leaders say they’re investing in agentic AI, the complex enterprise architectures of large organizations often struggle with the tech’s demands.

IBM targets agentic AI scale-up with new Enterprise Advantage consulting service

January 20, 2026: IBM has launched a new consulting service named Enterprise Advantage, designed to help CIOs take their agentic and other AI applications from experimentation to large-scale production.

EY exec: If you think agentic AI is a challenge, you’re not ready for what’s coming

January 15, 2026: Companies struggling to keep up with the arrival of AI agents should buckle up: Even more complicated agentic AI technologies are quickly coming down the pike. That includes physical AI, which includes robots and quantum computing.

Managing agentic AI risk: Lessons from the OWASP Top 10

December 19, 2025: LLM-powered chatbots have risks that we see playing out in the headlines on a nearly daily basis. But chatbots are limited to answering questions. AI agents, however, access data and tools and carry out tasks, making them infinitely more capable – and more dangerous to enterprises.

Agentic AI in 2026: More mixed than mainstream

December 18, 2025: Agentic AI is having its everything, everywhere, all at once moment. Or is it? Data clarifies. While 39% of organizations surveyed by McKinsey say they are experimenting with agents, only 23% have begun scaling AI agents within one business function

Overcome governance and trust issues to drive agentic AI

December 18, 2025: Fully autonomous agentic AI is still way off but AI agents are making inroads within enterprise software and workflows. Gartner predicts 40% of enterprise software will feature task-specific AI agents by the end of 2026 as the current trend for embedded AI assistants evolves.

Nvidia bets on open infrastructure for the agentic AI era with Nemotron 3

Decenber 15, 2025: AI agents must be able to cooperate, coordinate, and execute across large contexts and long time periods, and this, says Nvidia, demands a new type of infrastructure, one that is open. The company says it has the answer with its new Nemotron 3 family of open models.

Microsoft drops M365 Copilot price for SMBs, upgrades free Copilot Chat

November 19, 2025: Microsoft announced that it reduce the price of Microsoft 365 Copilot for small and mid-sized firms beginning next month. Microsoft 365 Copilot for Business will cost $21 per user, per month for customers with any Microsoft 365 Business plan. That’s down from the current $30 monthly price.

Microsoft Fabric IQ adds ‘semantic intelligence’ layer to Fabric

November 19, 2025: Microsoft promises enterprises better understanding of their data for workers and autonomous agents alike, but analysts fear deployment hurdles and vendor lock-in.

Microsoft unveils Agent 365 to help IT manage AI ‘agent sprawl’

November 18, 2025: As businesses begin deploying AI agents in greater numbers, IT teams will need to manage and secure those AI systems as they connect to corporate data. That’s the idea behind Microsoft’s Agent 365 (A365), a new “control plane” that lets customers deploy and govern the use of agents. 

From chatbots to colleagues: How agentic AI is redefining enterprise automation

November 17, 2025: A new wave of agentic AI is taking shape: systems that not only converse but also reason, plan, and act within enterprise workflows. These agents are not assistants that talk; they are digital colleagues that think.

The enterprise IT overhaul: Architecting your stack for the agentic AI era

November 10, 2025: For the CIO, the conversation has officially moved past the large language model (LLM). The next critical chapter is agentic AI — autonomous systems capable of reasoning, planning and executing multi-step tasks across your enterprise. Agentic AI is here. Now, CIOs must orchestrate

October 23, 2025: Agentic AI is about to change how companies create value. Yet, most enterprises aren’t ready. The problem isn’t the technology — it’s the planning and execution. Too many pilots stall out because CIOs haven’t built the AI systems, guardrails and culture to move beyond experiments.

AI agents might smooth some of retail’s worst data problems

October 21, 2025: So many retail challenges hinge on unreliable product data. Can agentic AI clean up that data enough to make a difference? Can it do the same for other verticals?

The impact of agentic AI on SaaS and partner ecosystems

October 16, 2025: The enterprise technology landscape is entering a critical pivot point as agentic AI transforms partner ecosystems from human-mediated, application integration networks into autonomous, self-orchestrating and intelligent ecosystems.

Salesforce updates its agentic AI pitch with Agentforce 360

October 13 2025: Salesforce announced a new release of Agentforce that, it says, “gives teams the fastest path from AI prototypes to production-scale agents” — although with many of the new release’s features still to come, or yet to enter pilot phases or beta testing, some parts of that path will be much slower than others.

Gemini Enterprise is Google’s new ‘front door’ for agentic AI access at work

October 9, 2025: Google introduced an AI assistant to serve as a platform so users can access and coordinate AI agents that automate work tasks. Gemini Enterprise, which replaces the Agentspace app launched last year, also features new enterprise search functions to help customers tap into data from across an organization’s business apps. 

Oracle’s agentic AI push in Fusion Cloud CX offers embedded automation for CX leaders

October 7, 2025: Oracle is adding new pre-built agents to its Advertising and Customer Experience Cloud (Fusion Cloud CX) to help enterprises increase operational efficiency by automating sales, service, and marketing processes.

IBM touts agentic AI orchestration, cryptographic risk controls

October 7, 2025: IBM watsonx Orchestrate offers more than 500 tools and customizable, domain-specific agents from IBM and third-party contributors. Among the additions to watsonx Orchestrate are AgentOps capabilities that offer real-time monitoring and policy-based controls for observability and governance.

How self-learning AI agents will reshape operational workflows

October 6, 2025: Google’s recent whitepaper, “Welcome to the Era of Experience,” signals a shift in the way AI agents are trained. Google hypothesizes that allowing AI agents to learn from the experience of agents rather than solely from human-generated training data will enable autonomous AI to surpass its current capabilities.

Are your agentic AI projects driving toward success?

October 3, 2025: Anushree Verma, Gartner senior director analyst, says most agentic AI projects today are early-stage experiments or proofs of concept, fueled primarily by hype and often misapplied.

Microsoft unveils framework for building agentic AI apps

October 3. 2025: Microsoft has introduced the Microsoft Agent Framework, an open-source SDK and runtime for building, orchestrating, and deploying AI agents and multi-agent workflows, with full framework support for .NET and Python.

Salesforce Trusted AI Foundation seeks to power the agentic enterprise

October 2, 2025: As Salesforce pushes further into agentic AI, its aim is to evolve Salesforce Platform from an application for building AI to a foundational operating system for enterprise AI ecosystems. 

ServiceNow’s AI Experience is an agentic AI UI for the Now Platform

September 30, 2025: ServiceNow today launched the AI Experience (AIx), a contextually aware multimodal AI-driven use UI for its Now platform. Building on the ServiceNow AI Platform and with a foundation in Now Assist, the company describes it as “a unified, conversational front door to enterprise AI.”

How MCP is making AI agents actually do things in the real world

September 29, 2025: You’ve seen them: Those incredible large language models (LLMs) that can chat, write and even generate code. They’ve revolutionized how we interact with technology, but there’s a new, even more exciting chapter unfolding. Discover how MCP is turning chatbots into doers, and the future of work may never look the same.

Agentic AI in IT security: Where expectations meet reality

September 29, 2025: Agentic AI has shifted from lab demos to real-world SOC deployments. Unlike traditional automation scripts, software agents are designed to act on signals and execute security workflows intelligently, correlating logs, enriching alerts, and even take first-line containment actions.

Walmart looks to cash in on agentic AI

September 19, 2025: Walmart doesn’t intend to lose its retail crown anytime soon. And, according to US EVP and CTO Hari Vasudev, the $815B company’s artificial intelligence strategy will play a key role in preventing that from happening.

5 steps for deploying agentic AI red teaming

September 17, 2025: As more enterprises deploy agentic AI applications, the potential attack surface increases in complexity and reach. But there is still hope that AI agents can be harnessed for defensive purposes too, including using traditional red teaming and penetration testing techniques but updated for the AI world.

Google unveils payments protocol for AI agents with major financial firms

September 17. 2025: Google has introduced the Agent Payments Protocol (AP2), an open framework developed with more than 60 payments and technology companies to support secure, agent-led transactions across platforms and payment methods.

CrowdStrike bets big on agentic AI with new offerings after $290M Onum buy

September 16, 2025: At its Fal.Con conference, the cybersecurity giant launched its Agentic Security Platform and Agentic Security Workforce, aiming to outpace AI-driven adversaries with real-time intelligence, automation, and a common language for defense.

Adobe makes Agent Orchestrator and AI agents generally available

September 10, 2025: Adobe Experience Platform (AEP) Agent Orchestrator and six new AI agents are designed to build, deliver, and optimize customer experience and marketing campaigns. The company also announced Experience Platform Agent Composer for customizing and configuring AI agents based on brand guidelines and organizational policy.

Rethinking the IT organization for the agentic AI era

September 2, 2025: With the advent of agentic AI, CIOs must be poised to adjust strategic IT priorities, mitigate new security risks, and reskill staff for a new era.

How to build a production-grade agentic AI platform

September 2, 2025: Modular orchestration, fail-safe design, hybrid memory management, and LLM integration with domain knowledge are essential to agentic AI systems that reason, act, and adapt at scale.

Agentic AI: A CISO’s security nightmare in the making?

September 2, 2025: Enterprises will no doubt be using agentic AI for a growing number of workflows and processes, including software development, customer support automation, and more. But what are the cybersecurity risks of agentic AI, and how much more work will it take for them to support their organizations’ agentic AI dreams?

Microsoft researchers develop new tech for video AI agents

September 2, 2025: Microsoft researchers are developing technologies for a new class of video AI agents to explore three-dimensional spaces before making decisions.The technology framework, called MindJourney, uses a range of AI technologies to understand and analyze 3D spaces, reason about the surroundings, and predict movement

Salesforce AI Research unveils new tools for AI agents

August 27, 2025: Salesforce announced a simulated enterprise environment, benchmark, and account data unification tool that are designed to help customers transform into agentic AI enterprises.

Agentic AI promises a cybersecurity revolution — with asterisks

August 18, 2025: The hottest topic at this year’s Black Hat conference was the meteoric emergence of AI tools for both cyber adversaries and defenders, particularly the use of agentic AI to strengthen cybersecurity programs.

4 thoughts on who should manage AI agents

August 11, 2025: As AI agents proliferate, we need to turn our attention beyond AI agent builder platforms to AI orchestration and AI GRC platforms. It also raises questions about which groups within the enterprise should manage AI agents and how they should be treated.

How bright are AI agents? Not very, recent reports suggest

July 31, 2025: Security researchers are adding more weight to a truth that infosec pros had already grasped: AI agents are not very bright, and are easily tricked into doing stupid or dangerous things

Will AI agents eat the SaaS market? Experts are split

July 31,2025: As hype about AI agents reaches new heights, an emerging theory suggests that the groundbreaking AI tools will kill the SaaS business model. The claim isn’t particularly new, but is resurfacing, with people like Microsoft CEO Satya Nadella voicing this position. 

How agentic AI will change database management

July 28, 2025: Generative AI has already had a profound impact on the world of database management. And now, thanks to AI’s knack for pattern-recognition, teams can use generative AI to analyze data sets, detect anomalies, and access invaluable insights with record speed and precision. 

As AI agents go mainstream, companies lean into confidential computing for data security

July 21, 2025: Companies need to stop ignoring data security as AI agents take over internal data movement in IT environments, analysts and IT execs warn. To address that issue, some tech players are embracing the concept of “confidential computing.” While it’s existed for years, it;s now finding new life with the rise of genAI.

How agentic AI will transform mobile apps and field operations

July 15, 2015: Agentic AI will usher in new mobile AI experiences. Construction, manufacturing, healthcare, and other industries with significant field operations will benefit from mobile AI agents and the resulting operational agility. 

MCP is fueling agentic AI — and introducing new security risks

July 10, 2025: Model Context Protocol (MCP) has caught fire, with several thousand MCP servers now available from a wide range of vendors enabling AI assistants to connect to their data and services. And with agentic AI increasingly seen as the future of IT, MCP will only grow in use in the enterprise. But innovations like MCP also come with significant security risks.

3 industries where agentic AI is poised to make its mark

July 4, 2024:  IT leaders from finance, retail, and healthcare lend insights into what organizations are doing with AI agents today — and where they see the technology taking their organizations and industries in the future.

IFS rolls TheLoops agentic AI into industrial ERP

June 27, 2025: IFS is adding AI agent development and management capabilities to its ERP platform with the acquisition of software startup The acquisition brings TheLoops’ full Agent Development life cycle (ADLC) platform into IFS, enabling enterprises to design, test, deploy, monitor, and fine-tune AI agents with built-in support for versioning, compliance, and performance optimization.

How AI agents and agentic AI differ from each other

June 12, 2025: With agentic AI in its infancy and organizations rushing to adopt AI agents, there seems to be confusion about the difference between “agentic AI” and “AI agents” technologies, but experts say there’s growing understanding that the two are separate, but related, tools.

The future of RPA ties to AI agents

June 10, 2025: RPA is accelerating toward a crossroads, with IT leaders and experts debating its future. Some IT leaders say that more powerful and autonomous AI agents will replace the two-decade-old AI precursor technology, while others predict that AI agents and RPA will work hand-in-hand.

MCP is enabling agentic AI, but how secure is it?

June 2, 2025: Model context protocol (MCP) is becoming the plug-and-play standard for agentic AI apps to pull in data in real time from multiple sources. However, this also makes it more attractive for malicious actors looking to exploit weaknesses in how MCP has been deployed. 

The agentic AI assist Stanford University cancer care staff needed

May 30, 2025: At Microsoft Build 2025 earlier this month, Nigam Shah, CDO for Stanford Health Care, discussed agentic AI’s ability to redefine healthcare, especially in oncology, as physicians get overloaded with the administrative tasks of medicine, he said, which lead to burnout.

Agentic AI, LLMs and standards big focus of Red Hat Summit

May 26, 2025: Red Hat, announced a number of improvements in its core enterprise Linux product, including better security, better support for containers, better support for edge devices. But the one topic that dominated the conversation was AI.

Putting agentic AI to work in Firebase Studio

May 21, 2025: Putting agentic AI to work in software engineering can be done in a variety of ways. Some agents work independently of the developer’s environment, working essentially like a remote developer. Other agents directly within a developer’s own environment. Google’s Firebase Studio is an example of the latter, drawing on Google’s Gemini LLM o help developers prototype and build applications .

Why is Microsoft offering to turn websites into AI apps with NLWeb?

May 20. 2025: NLWeb, short for Natural Language Web, is designed to help enterprises build a natural language interface for their websites using the model of their choice and data to answer user queries about the contents of the website. Microsoft hopes to stake its claim on the agentic web before rivals Google and Amazon do.

Databricks to acquire open-source database startup Neon to build the next wave of AI agents

May 14, 2025: Agentic AI requires a new type of architecture because traditional workflows create gridlock, dragging down speed and performance. To get ahead in this next generation of app building, Databricks announced it will purchase Neon, an open-source serverless Postgres company.

Agentic mesh: The future of enterprise agent ecosystems

May 13, 2025: Nvidia CEO Jensen Huang predicts we’ll soon see “a couple of hundred million digital agents” inside the enterprise. Microsoft CEO Satya Nadella takes it even further: “Agents will replace all software.”

Google to unveil AI agent for developers at I/O, expand Gemini integration

May 13, 2025: Google is expected to unveil a new AI agent aimed at helping software developers manage tasks across the coding lifecycle, including task execution and documentation. The tool has reportedly been demonstrated to employees and select external developers ahead of the company’s annual I/O conference.

Nvidia, ServiceNow engineer open-source model to create AI agents

May 6, 2025: Nvidia and ServiceNow have created an AI model that can help companies create learning AI agents to automate corporate workloads. The open-source Apriel model, available generally in the second quarter on HuggingFace, will help create AI agents that can make decisions around IT, human resources and customer-service functions.

How IT leaders use agentic AI for business workflows

April 30, 2025: Jay Upchurch, CIO at SAS, backs agentic AI to enhance sales, marketing, IT, and HR motions. “Agentic AI can make sales more effective by handling lead scoring, assisting with customer segmentation, and optimizing targeted outreach,” he says.

Microsoft sees AI agents shaking up org charts, eliminating traditional functions

April 28, 2025: As companies increasingly automate work processes using agents, traditional functions such as finance, marketing, and engineering may fall away, giving rise to an ‘agent boss’ era of delegation and orchestration of myriad bots.

Cisco automates AI-driven security across enterprise networks

April 28, 2025: Cisco announced a range of AI-driven security enhancements, including improved threat detection and response capabilities in Cisco XDR and Splunk Security, new AI agents, and integration between Cisco’s AI Defense platform and ServiceNow SecOps.

Hype versus execution in agentic AI

April 25, 2025: Agentic AI promises autonomous systems capable of reasoning, making decisions, and dynamically adapting to changing conditions. The allure lies in machines operating independently, free of human intervention, streamlining processes and enhancing efficiency at unprecedented scales. But David Linthicum writes, don’t be swept up by ambitious promises. 

Agents are here — but can you see what they’re doing?

April 23, 2025: As the agentic AI models powering individual agents get smarter, the use cases for agentic AI systems get more ambitious — and the risks posed by these systems increase exponentially.A multicloud experiment in agentic AI: Lessons learned

Agentic AI might soon get into cryptocurrency trading — what could possibly go wron

April 15, 2025: Agentic AI promises to simplify complex tasks such as crypto trading or managing digital assets by automating decisions, enhancing accessibility, and masking technical complexity.

Agentic AI is both boon and bane for security pros

April 15, 2025: Cybersecurity is at a crossroads with agentic AI. It’s a powerful tool that can create reams of code in a blink of an eye, find and defuse threats, and be used so decisively and defensively. This has proved to be a huge force multiplier and productivity boon. But while powerful, agentic AI isn’t dependable, and that is the conundrum. 

AI agents vs. agentic AI: What do enterprises want?

April 15, 2025:  Now that this AI agent story has morphed into “agentic AI,” it seems to have taken on the same big-cloud-AI flavor that enteriprise already rejected. What do they want from AI agents, why is “agentic” thinking wrong, and where is this all headed?

A multicloud experiment in agentic AI: Lessons learned

April 11, 2025: Turns out you really can build a decentralized AI system that operates successfully across multiple public cloud providers. It’s both challenging and costly.

Google adds open source framework for building agents to Vertex AI

April 9, 2025: Google is adding a new open source framework for building agents to its AI and machine learning platform Vertex AI, along with other updates to help deploy and maintain these agents. The open source Agent Development Kit (ADK) will make it possible to build an AI agent in under 100 lines of Python code. It expects to add support for more languages later this year.

Google’s Agent2Agent open protocol aims to connect disparate agents

April 9, 2025: Google has taken the covers off a new open protocol — Agent2Agent (A2A) — that aims to connect agents across disparate ecosystems.. At its annual Cloud Next conference, Google said that the A2A protocol will enable enterprises to adopt agents more readily as it bypasses the challenge of agents that are built on different vendor ecosystems not being able to communicate with each other.

Riverbed bolsters AIOps platform with predictive and agentic AI

April 8, 2025: Riverbed unveiled updates to its AIOps and observability platform that the company says will transform how IT organizations manage complex distributed infrastructure and data more efficiently. Expanded AI capabilities are aimed at making it easier to manage AIOps and enabling IT organizations to transition from reactive to predictive IT operations.

Microsoft’s newest AI agents can detail how they reason

March 26, 2025: If you’re wondering how AI agents work, Microsoft’s new Copilot AI agents provide real-time answers on how data is being analyzed and sourced to reach results. The Researcher and Analyst agents take a deeper look at data sources such as email, chat or databases within an organization to produce research reports, analyze strategies, or convert raw information into meaningful data.

Microsoft launches AI agents to automate cybersecurity amid rising threats

March 26, 2025: Microsoft has introduced a new set of AI agents for its Security Copilot platform, designed to automate key cybersecurity functions as organizations face increasingly complex and fast-moving digital threats. The new tools focus on tasks such as phishing detection, data protection, and identity management.

How AI agents work

March 24, 2025: By leveraging technologies such as machine learning, natural language processing (NLP), and contextual understanding, AI agents can operate independently, even partnering with other agents to perform complex tasks.

5 top business use cases for AI agents

March 19, 2025: AI agents are poised to transform the enterprise, from automating mundane tasks to driving customer service and innovation. But having strong guardrails in place will be key to success.

Nvidia launches AgentIQ toolkit to connect disparate AI agents

March 21, 2025: As enterprises look to adopt agents and agentic AI to boost the efficiency of their applications, Nvidia this week introduced a new open-source software library — AgentIQ toolkit — to help developers connect disparate agents and agent frameworks..

Deloitte unveils agentic AI platform

March 18, 2025: At Nvidia GTC 2025 in San Jose, Deloitte announced Zora AI, a new agentic AI platform that offers a portfolio of AI agents for finance, human capital, supply chain, procurement, sales and marketing, and customer service.The platform draws on Deloitte’s experience from its technology, risk, tax, and audit businesses, and is integrated with all major enterprise software platforms. 

The dawn of agentic AI: Are we ready for autonomous technology?

March 15, 2025: Much of the AI work prior has focused on large language models (LLMs) with a goal to give prompts to get knowledge out of the unstructured data. So it’s a question-and-answer process. Agentic AI goes beyond that. You can give it a task that might involve a complex set of steps that can change each time.

How to know a business process is ripe for agentic AI

March 11, 2025: Deloitte predicts that in 2025, 25% of companies that use generative AI will launch agentic AI pilots or proofs of concept, growing to 50% in 2027. The firm says some agentic AI applications, in some industries and for some use cases, could see actual adoption into existing workflows this year.

With new division, AWS bets big on agentic AI automation

March 6, 2025: Amazon Web Services customers can expect to hear a lot more about agentic AI from AWS in future with the news that the company is setting up a dedicated unit to promote the technology on its platform.

How agentic AI makes decisions and solves problems

March 6, 2025: GenAI’s latest big step forward has been the arrival of autonomous AI agents. Agentic AI is based on AI-enabled applications capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. 

CIOs are bullish on AI agents. IT employees? Not so much

Feb. 4, 2025: Most CIOs and CTOs are bullish on agentic AI, believing the emerging technology will soon become essential to their enterprises, but lower-level IT pros who will be tasked with implementing agents have serious doubts.

The next AI wave — agents — should come with warning labels. Is now the right time to invest in them?

Jan.13, 2025: The next wave of artificial intelligence (AI) adoption is already under way, as AI agents — AI applications that can function independently and execute complex workflows with minimal or limited direct human oversight — are being rolled out across the tech industry.

AI agents are unlike any technology ever

Dec. 1, 2024: The agents are coming, and they represent a fundamental shift in the role artificial intelligence plays in businesses, governments, and our lives.

AI agents are coming to work — here’s what businesses need to know

Nov. 21, 2024: AI agents will soon be everywhere, automating complex business processes and taking care of mundane tasks for workers — at least that’s the claim of various software vendors that are quickly adding intelligent bots to a wide range of work apps.

Agentic AI swarms are headed your way

November 1, 2024: OpenAI launched an experimental framework called Swarm. It’s a “lightweight” system for the development of agentic AI swarms, which are networks of autonomous AI agents able to work together to handle complex tasks without human intervention, according to OpenAI. 

Is now the right time to invest in implementing agentic AI?

October 31, 2024: While software vendors say their current agentic AI-based offerings are easy to implement, analysts say that’s far from the truth

Let Agentic AI speak unto Agentic AI — but in some kind of mathematical code.

That’s the thinking behind the MATHBAC (Mathematics for Boosting Agentic Communication) project, which aims to develop a new area of AI communication, one in which AI agents will ‘talk’ to each other to understand how they collaborate and share information.

MATHBAC is being run by the US Defense Advanced Research Projects Agency (DARPA), one of the progenitors of the internet. It hopes the research will enable agentic AI models to collaborate to solve complex problems, and increase understanding of the mathematics that lies behind the ways that they function.

A key element of the project is discovering fundamentally new ways of working: Research that results only in incremental improvements in existing methods and models that already exist is specifically excluded from MATHBAC funding.

The project has been divided into two phases. The first will consider the derivation of the mathematics behind agentic AI and look at ways of improving communication between systems. The second, much more ambitious, will look to create tools to enable development of a new science, solving “fundamental scientific and mathematical problems underpinning collective agentic intelligence.”

DARPA expects to achieve all that in just 34 months, and is accepting proposals from organizations wishing to work on the project.

The attack surface targeted by Iranian-linked hackers in cyberattacks against U.S. critical infrastructure networks includes thousands of Internet-exposed programmable logic controllers (PLCs) manufactured by Rockwell Automation. [...]

Nevýhodou žebříčků postavených na sledovanosti je, že jsou plné prastarých kousků, které už každý viděl. Tady proto najdete pouze nové filmy a seriály (nebo jejich nové sezóny) z posledních měsíců. Vycházíme ze statistik aktuálního zájmu na webu IMDB, které dlouhodobě ukládáme a sami zpracováváme.

Though I reviewed Apple’s recently-introduced MacBook Neo, M5 MacBook Air, and M5 Max MacBook Pro, I didn’t look at Apple’s new displays. But it is noteworthy that even these products open up new opportunities for the company.

That’s because Apple this week gained FDA clearance for the Medical Imaging Calibration feature introduced in the Studio Display XDR. Just as the affordable MacBook Neo opens up a fresh mass market opportunity, this specialized product feature forges space in a new niche. 

Apple opens a new growth market

That niche will only become even more important once specialized AI medical tools to support treatment and diagnosis appear in the radiology space, as such tools inevitably will. (Inevitably? I mean, just look at this December research announcement from the Institute of Cancer Research, which demonstrated that combining artificial intelligence (AI) with state-of-the-art MRI imaging can revolutionize prostate cancer treatment.)

The combination means you don’t need a dedicated radiology workstation costing in excess of $15,000; you need only a Mac and a $2,899 Apple display. 

Once those things are in place, you can select your choice of imaging software — probably something like Visage Imaging 7, OsiriX MD, Falcon MD, or another of the solutions available for Mac. Even better, while privacy and data confidentiality concerns do exist, the Mac you use for this work can also be used for other tasks, like any other Mac. This democratizes access to tools of this kind; gives the medical profession all the Apple advantages around product resilience, TCO, and tech support; cuts budgets; and enables medical tech purchasers to get more for less.

On-device AI, an Apple advantage

Then we get to think about AI, and that’s where Apple’s strategic sensibility seems to be coming into play. I see it like this: it is obvious that AI for medical imaging will need to run on something. And what Apple has done with Apple Silicon, its approach to on-device AI, and this new medical image calibration feature on its displays all mean it now offers a trusted, highly usable, incredibly flexible solution to run future AI-augmented MRI imaging packages. Apple’s processors can simply shrug their way through that kind of work, while its new displays can give radiologists and other medical examiners the precise accuracy they need.

The new display feature also gives Apple an impressive story to tell in the $42.6 billion global market for medical imaging devices. That tale is tempered by Apple’s cast-iron commitment to privacy and the impressive capacity of Apple Silicon to run on-device LLMs. Apple’s introduction of MLX means you can easily imagine medical imaging deployments that rely on a new Studio Display and four Mac minis clustered via a single Thunderbolt 5 cable. Total cost? Not $15,000. 

Apple is cheap

Apple is cheap. That’s not an illusion. Look at the ecosystem. The entry level $599 MacBook Neo shows this, while all the TCO and tech support and security studies I’ve seen across the last decade show that once you begin using these platforms you end up spending a lot less keeping your investments going.

That matters in any business, of course. But when it comes to the kind of industries Medical Imaging Calibration is meant for, that can be life-saving. Who wants urgent surgery to be delayed by an operating system crash or another Crowdstrike-like moment?

There might be problems getting this message through to every medical provider across the planet, but check out Emory University and its Department of Radiology and Imaging Sciences. There, you can peruse a white paper explaining most of the steps radiologists and imaging practices must take to integrate Apple’s displays and systems into their clinical workflows. 

As explained here, the paper praises the CPU/GPU performance of Macs used in the test, which rival or exceed traditional workstations at a fraction of the cost. The white paper also opens a second dimension in medical practice, thanks to visionOS and the capacity to create new workflows that have the headset using new surgical apps from the likes of Stryker and Storz. Add AI to that equation and you can see that Apple has raised a very, very large flag depicting a very large Apple logo on part of the future of medical care. 

Please follow me on Twitter, or join me in the AppleHolic’s bar & grill and Apple Discussions groups on MeWe. Also, now on Mastodon.

Posted by Jiacheng Lu, Software Engineer, Google Pixel Team

Google is continuously advancing the security of Pixel devices. We have been focusing on hardening the cellular baseband modem against exploitation. Recognizing the risks associated within the complex modem firmware, Pixel 9 shipped with mitigations against a range of memory-safety vulnerabilities. For Pixel 10, Google is advancing its proactive security measures further. Following our previous discussion on "Deploying Rust in Existing Firmware Codebases", this post shares a concrete application: integrating a memory-safe Rust DNS(Domain Name System) parser into the modem firmware. The new Rust-based DNS parser significantly reduces our security risk by mitigating an entire class of vulnerabilities in a risky area, while also laying the foundation for broader adoption of memory-safe code in other areas.

Here we share our experience of working on it, and hope it can inspire the use of more memory safe languages in low-level environments.

Why Modem Memory Safety Can’t Wait

In recent years, we have seen increasing interest in the cellular modem from attackers and security researchers. For example, Google's Project Zero gained remote code execution on Pixel modems over the Internet. Pixel modem has tens of Megabytes of executable code. Given the complexity and remote attack surface of the modem, other critical memory safety vulnerabilities may remain in the predominantly memory-unsafe firmware code.

Why DNS?

The DNS protocol is most commonly known in the context of browsers finding websites. With the evolution of cellular technology, modern cellular communications have migrated to digital data networks; consequently, even basic operations such as call forwarding rely on DNS services.

DNS is a complex protocol and requires parsing of untrusted data, which can lead to vulnerabilities, particularly when implemented in a memory-unsafe language (example: CVE-2024-27227). Implementing the DNS parser in Rust offers value by decreasing the attack surfaces associated with memory unsafety.

Picking a DNS library

DNS already has a level of support in the open-source Rust community. We evaluated multiple open source crates that implement DNS. Based on criteria shared in earlier posts, we identified hickory-proto as the best candidate. It has excellent maintenance, over 75% test coverage, and widespread adoption in the Rust community. Its pervasiveness shows its potential as the de-facto DNS choice and long term support. Although hickory-proto initially lacked no_std support, which is needed for Bare-metal environments (see our previous post on this topic), we were able to add support to it and its dependencies.

Adding no_std support

The work to enable no_std for hickory-proto is mostly mechanical. We shared the process in a previous post. We undertook modifications to hickory_proto and its dependencies to enable no_std support. The upstream no_std work also results in a no_std URL parser, beneficial to other projects.

• https://github.com/hickory-dns/hickory-dns/pull/2104
• https://github.com/servo/rust-url/pull/831
• https://github.com/krisprice/ipnet/pull/58

The above PRs are great examples of how to extend no_std support to existing std-only crates.

Code size study

Code size is the one of the factors that we evaluated when picking the DNS library to use.

Code size
by category Rust implemented Shim that calls Hickory-proto on receiving a DNS response 4KB core, alloc, compiler_builtins
(reusable, one-time cost) 17KB Hickory-proto library and dependencies 350KB

Sum 371KB

We built prototypes and measured size with size-optimized settings. Expectedly, hickory_proto is not designed with embedded use in mind, and is not optimized for size. As the Pixel modem is not tightly memory constrained, we prioritized community support and code quality, leaving code size optimizations as future work.

However, the additional code size may be a blocker for other embedded systems. This could be addressed in the future by adding additional feature flags to conditionally compile only required functionality. Implementing this modularity would be a valuable future work.

Hook-up Rust to modem firmware

Before building the Rust DNS library, we defined several Rust unit tests to cover basic arithmetic, dynamic allocations, and FFI to verify the integration of Rust with the existing modem firmware code base.

Compile Rust code to staticlib

While using cargo is the default choice for compilation in the Rust ecosystem, it presents challenges when integrating it into existing build systems. We evaluated two options:

1. Using cargo to build a staticlib before the modem builds. Then add the produced staticlib into the linking step.
2. Directly work with rustc and integrate the Rust compilation steps into the existing modem build system.

Option #1 does not scale if we are going to add more Rust components in the future, as linking multiple staticlibs may cause duplicated symbol errors. We chose option #2 as it scales more easily and allows tighter integration into our existing build system. Our existing C/C++ codebase uses Pigweed to drive the primary build system. Pigweed supports Rust targets (example) with direct calls to rustc through rust tools defined in GN.

We compiled all the Rust crates, including hickory-proto, its dependencies, and core, compiler_builtin, alloc, to rlib. Then, we created a staticlib target with a single lib.rs file which references all the rlib crates using extern crate keywords.

Build core, alloc, and compiler_builtins

Android’s Rust Toolchain distributes source code of core, alloc, and compiler_builtins, and we leveraged this for the modem. They can be included to the build graph by adding a GN target with crate_root pointing to the root lib.rs of each crate.

Pixel modem firmware already has a well-tested and specialized global memory allocation system to support some dynamic memory allocations. alloc support was added by implementing the GlobalAlloc with FFI calls to the allocators C APIs:

use core::alloc::{GlobalAlloc, Layout}; extern "C" { fn mem_malloc(size: usize, alignment: usize) -> *mut u8; fn mem_free(ptr: *mut u8, alignment: usize); } struct MemAllocator; unsafe impl GlobalAlloc for MemAllocator { unsafe fn alloc(&self, layout: Layout) -> *mut u8 { mem_malloc(layout.size(), layout.align()) } unsafe fn dealloc(&self, ptr: *mut u8, layout: Layout) { mem_free(ptr, layout.align()); } } #[global_allocator] static ALLOCATOR: MemAllocator = MemAllocator;

Pixel modem firmware already implements a backend for the Pigweed crash facade as the global crash handler. Exposing it into Rust panic_handler through FFI unifies the crash handling for both Rust and C/C++ code.

#![no_std] use core::panic::PanicInfo; extern "C" { pub fn PwCrashBackend(sigature: *const i8, file_name: *const i8, line: u32); } #[panic_handler] fn panic(panic_info: &PanicInfo) -> ! { let mut filename = ""; let mut line_number: u32 = 0; if let Some(location) = panic_info.location() { filename = location.file(); line_number = location.line(); } let mut cstr_buffer = [0u8; 128]; // Never writes to the last byte to make sure `cstr_buffer` is always zero // terminated. let (_, writer) = cstr_buffer.split_last_mut().unwrap(); for (place, ch) in writer.iter_mut().zip(filename.bytes()) { *place = ch; } unsafe { PwCrashBackend( "Rust panic\0".as_ptr() as *const i8, cstr_buffer.as_ptr() as *const i8, line_number, ); } loop {} } Link Rust staticlib

The Pixel modem firmware linking has a step that calls the linker to link all the objects generated from C/C++ code. By using llvm-ar -x to extract object files from the Rust combined staticlib and supplying them to the linker, the Rust code appears in the final modem image.

There was a performance issue we experienced due to weak symbols during linking. The inclusion of Rust core and compiler-builtin caused unexpected power and performance regressions on various tests. Upon analysis, we realized that modem optimized implementations of memset and memcpy provided by the modem firmware are accidentally replaced by those defined in compiler_builtin. It seems to happen because both compiler_builtin crate and the existing codebase defines symbols as weak, linker has no way to figure out which one is weaker. We fixed the regression by stripping the compiler_builtin crate before linking using a one line shell script.

llvm-ar -t <rust staticlib> | grep compiler_builtins | xargs llvm-ar -d <rust staticlib> Integrating hickory-proto Expose Rust API and calling back to C++

For the DNS parser, we declared the DNS response parsing API in C and then implemented the same API in Rust.

int32_t process_dns_response(uint8_t*, int32_t);

The Rust function returns an integer standing for the error code. The received DNS answers in the DNS response are required to be updated to in-memory data structures that are coupled with the original C implementation, therefore, we use existing C functions to do it. The existing C functions are dispatched from the Rust implementation.

pub unsafe extern "C" fn process_dns_response( dns_response: *const u8, response_len: i32, ) -> i32 { //... validate inputs `dns_response` and `response_len`. // SAFETY: // It is safe because `dns_response` is null checked above. `response_len` // is passed in, safe as long as it is set correctly by vendor code. match process_response(unsafe { slice::from_raw_parts(dns_response, response_len) }) { Ok(()) => 0, Err(err) => err.into(), } } fn process_response(response: &[u8]) -> Result<()> { let response = hickory_proto::op::Message::from_bytes(response)?; let response = hickory_proto::xfer::DnsResponse::from_message(response)?; for answer in response.answers() { match answer.record_type() { hickory_proto::RecordType:... => { // SAFETY: // It is safe because the callback function does not store // reference of the inputs or their members. unsafe { callback_to_c_function(...)?; } } // ... more match arms omitted. } } Ok(()) }

In our case, the DNS responding parsing function API is simple enough for us to hand write, while the callbacks back to C functions for handling the response have complex data type conversions. Therefore, we leveraged bindgen to generate FFI code for the callbacks.

Build third-party crates

Even with all features disabled, hickory-proto introduces more than 30 dependent crates. Manually written build rules are difficult to ensure correctness and scale poorly when upgrading dependencies into new versions.

Fuchsia has developed cargo-gnaw to support building their third party Rust crates. Cargo-gnaw works by invoking cargo metadata to resolve dependencies, then parse and generate GN build rules. This ensures correctness and ease of maintenance.

Conclusion

The Pixel 10 series of phones marks a pivotal moment, being the first Pixel device to integrate a memory-safe language into its modem.

While replacing one piece of risky attack surface is itself valuable, this project lays the foundation for future integration of memory-safe parsers and code into the cellular baseband, ensuring the baseband’s security posture will continue to improve as development continues.

Special thanks to Armando Montanez, Bjorn Mellem, Boky Chen, Cheng-Yu Tsai, Dominik Maier, Erik Gilling, Ever Rosales, Hungyen Weng, Ivan Lozano, James Farrell, Jeffrey Vander Stoep, Jiacheng Lu, Jingjing Bu, Min Xu, Murphy Stein, Ray Weng, Shawn Yang, Sherk Chung, Stephan Chen, Stephen Hines.

Dosažení hranice jedné miliardy uživatelů trvá dnes nepoměrně kratší dobu • Moderní aplikace umělé inteligence dokážou tento milník překonat nejrychleji • Pozor ale na odchylky v metodologii počítání stažení a uživatelů

Analysis of 1 billion CISA KEV remediation records reveal a breaking point for human-scale security. Qualys shows most critical flaws are exploited before defenders can patch them. [...]

Wii, jako jedna z nejúspěšnějších konzolí od Nintenda, toho za svoji dlouhou existenci zažilo už hodně. A teď na seznam přibyla další položka.

Cybersecurity researchers have flagged yet another evolution of the ongoing GlassWorm campaign, which employs a new Zig dropper that's designed to stealthily infect all integrated development environments (IDEs) on a developer's machine. The technique has been discovered in an Open VSX extension named "specstudio.code-wakatime-activity-tracker," which masquerades as WakaTime, a Ravie Lakshmananhttp://www.blogger.com/profile/[email protected]

When voters in the forthcoming Hungarian election assess the current government, its record on internet security will not be one of its proudest achievements.

An analysis by open source investigation organization Bellingcat has revealed that the passwords for almost 800 Hungarian government email accounts are circulating online, many of them associated with national security. These breaches in security are not down to high-tech attacks but rather are the result of poor email hygiene among government employees. The security leaks were widespread: 12 out of 13 government departments were affected.

Hungarian Prime Minister Viktor Orban’s administration likes to present itself as firm protector of Hungarian borders, resisting foreign interference, but this doesn’t seem to apply to its computing prowess. Among those whose details were revealed were an officer responsible for information security and a counter-terrorism expert.

Bellingcat found that government officials have been using weak passwords such as variations of the word “Password” or the number sequence “1234567, while another simply used his surname.

The Hungarian government is not alone in its laxity.  Earlier this year, Specops found that 6 billion logins had been exposed online and found that number sequences and ‘password’ featured highly in the list of the most compromised logins.

The vulnerabilities inherent in the Hungarian example are a warning to all CSOs that they should be reminding their staff to tighten their security credentials. Many choose simple, short memorable passwords because they’re easy to remember but using a password manager or deploying passkeys will immediately strengthen employees’ ability to protect data.

This article first appeared on CSO.

Hackers gained access to an API for the CPUID project and changed the download links on the official website to serve malicious executables for the popular CPU-Z and HWMonitor tools. [...]