Analysis threats

Significant cybersecurity M&A deals announced by Barracuda, CrowdStrike, Cyera, Okta, Palo Alto Networks, and Qualcomm. The post Cybersecurity M&A Roundup: 21 Deals Announced in July 2026 appeared first on SecurityWeek .

This content discusses a security concern related to autonomous AI agents that use large language models (LLMs) for decision-making and execution. It highlights the inherent risks of relying on probabilistic LLM-based guardrails to enforce security policies, which can be manipulated through indirect prompt injection or telemetry poisoning. The proposed solution is to decouple intent generation from execution by implementing deterministic policy gateways that enforce static, immutable authorization checks outside the LLM reasoning loop. This approach aims to prevent adversarial manipulation and unintended self-denial-of-service conditions in AI-driven environments.

This content is a write-up by a security researcher describing their approach to solving a pwn.college MITM challenge. The author overengineered the solution by manually managing TCP connections and sequence numbers instead of using the intended ARP spoofing and transparent forwarding method. The write-up includes technical details about the challenges faced, including an ACK sequence-space bug that disrupted the connection.

In the past few weeks, I have been using Gemma4 as a Large Language Model (LLM) to see how useful it can be to analyze some of the malware hashes uploaded to the DShield sensor over the past 30 days and figure out how its recommendation can be considered useful about the activity my DShield sensor is collecting and tracking. The model I use for this testing is gemma4:e4b [2] using two sites to compare the data against VirusTotal and CyberGordon.

This report discusses the security risk posed by fake remote workers exploiting gaps in the hiring process. Attackers use false identities to gain access to organizations by taking advantage of delays or weaknesses between hiring checks, device delivery, and account provisioning. The article highlights the importance of document verification and biometric liveness checks to ensure that the individual receiving access is the legitimate new hire. This threat targets organizational onboarding procedures rather than a specific software vulnerability.

A survey of 200 US-based CISOs, CTOs, and other senior security leaders at large organizations reveals widespread concern about new security risks introduced by AI deployments. The majority (93%) are worried about these risks, but only a small fraction (15%) are confident their current security tools can handle them. Key issues include a growing attack surface driven by complex non-human identities and insufficient access controls for machine workloads. The survey highlights operational challenges such as lengthy network change management delays and a widening patch gap. Most organizations are actively exploring new approaches to secure non-human identities, as existing identity systems are largely deemed insufficient.

This report highlights a potential resurgence of an old attack surface involving the creation of .desktop files by AI agents. These files, historically risky on Linux desktops, can be manipulated to perform UI spoofing, auto-start malicious payloads, or masquerade as trusted files. The concern is that AI agents might be tricked into generating such files via hidden instructions, potentially leading to execution of malicious scripts. The risk stems from .desktop files being able to attach as file handlers or auto-launch, which could be exploited if not properly controlled. The discussion suggests treating .desktop file creation or modification as a critical operation requiring explicit user confirmation.

This research highlights a design compromise in the Windows kernel related to Supervisor Mode Access Prevention (SMAP). Specifically, it experimentally confirms that SMAP is effectively disabled during standard IOCTL dispatches, allowing a stack pivot into user-mode memory without triggering SMAP protections. This behavior stems from the Windows kernel not being originally built with SMAP in mind, and retrofitting it would require extensive changes. The finding is a confirmation of an architectural limitation rather than a newly discovered vulnerability with an active exploit.

This content discusses the risks associated with using AI tools to diagnose live Linux systems. It suggests an alternative approach of generating sosreports with the sos command and then analyzing these reports with AI tools instead of directly interacting with production systems. The discussion is exploratory and does not describe a specific vulnerability or exploit. No affected software versions or patches are identified.

Identity-based attacks drive 90% of incidents. Learn how modern attackers exploit identities and what SOC leaders can do to respond. The post Inside the Modern SOC: The Identity Front Door appeared first on Unit 42 .

A report by Claroty Team82 analyzed over 750,000 data center cyber-physical system (CPS) assets and found significant exposure risks. Approximately 18% of data center infrastructure assets are one network hop away from systems making risky outbound internet connections. Critical systems such as power distribution units and HVAC/cooling systems have high exposure rates. Many building management systems communicate over insecure protocols and run outdated firmware. Legacy protocols like BACnet and Modbus are widely used in OT control and IoT systems. Additionally, 23% of IoT devices contain known exploited vulnerabilities. Attackers can leverage indirect pathways such as IT footholds, third-party remote access, and trusted network relationships to reach operational infrastructure assets.

Gen's H1 2026 Threat Report examines two separate attack chains. One used compromised business inboxes and browser manipulation in a banking-malware campaign, while the other used clipboard hijacking to redirect cryptocurrency payments. [...]

Have you ever tried to open an “encrypted” email or an urgent document, only to realize with horror that your usual DOCX attachment is actually a file with the extension .docx.exe? Or maybe you received an email supposedly with an invoice, contract, or internal memo attached — only for the file to prove something entirely different from what it claimed to be? If so, you were likely targeted in a malware infection attempt. Cybercriminals routinely disguise malicious files as harmless documents and archives, banking on recipients blindly clicking without checking the extension. Kaspersky experts analyzed the file formats most frequently deployed in malicious email blasts to reveal what really lurks behind these extensions — and how attackers weaponize them in their campaigns. Important note: the extensions we’ll be discussing here are routinely used for completely legitimate files. For example, Windows executables normally end in .exe. However, we’re focusing specifically on scenarios where attackers disguise or spoof a file’s extension to pass it off as a totally different type of file. What extensions are most commonly found in malware? Our experts analyzed malicious email blasts from the beginning of 2026 to pinpoint the 15 most common dangerous file extensions. .exe .js .html .dll .bat .vbs .xls .pdf .jse .au3 .docx .htm .wsf .scr .lnk The top 15 file extensions used in malicious email blasts Let’s take a look at what files with these extensions actually do under the hood. Executable files An executable file is a compiled computer program that’s ready to run. Once launched, a malicious executable can download additional payloads, alter system settings, steal user data, connect your device to external attacker-controlled servers, and much more. These are the most common executable extensions found in malicious email campaigns: .exe .dll .com .scr .exe The classic Windows executable extension. It powers every program you use daily, from web browsers and games to office suites and software installers. By the way, attackers often wrap EXE malware in double extensions: invoice.docx.exe, report.pdf.exe, or even photo.jpg.exe. They take advantage of a default Windows setting: hiding extensions for known file types. Because this setting is turned on by default, users only see the first part of the file name — invoice.docx, report.pdf, or photo.jpg — and assume it’s just a normal document or image. But the second you open this trap file, the malware fires up. .dll Another common extension abused in malicious campaigns is .dll (dynamic-link library). These libraries hold functions that Windows programs frequently require while running, such as printing a document. This modular architecture prevents redundant code by letting multiple applications call on the exact same library for specific tasks. However, if an attacker replaces a legitimate library with an infected one, running any normal program that calls on it can trigger malicious code. .com While files with the .com extension have absolutely nothing to do with the web domain of the same name, cybercriminals likely count on victims mistaking these files for links in an unusual format. In reality, it’s a legacy Windows executable format. While modern versions of Windows no longer rely on this file type, the operating system can still run and execute it, which makes opening one a very bad idea. .scr SCR files are screensavers — those idle Windows screen animations featuring abstract patterns, the iconic bouncing logo, or whatever else. Despite their harmless reputation, screensavers are essentially executables just like EXE files: once opened, they can install extra components or alter system settings all the same. In malicious campaigns, these files routinely masquerade as images, screenshots, or documents. Scripts Scripts are text files containing a sequence of commands that a computer runs automatically in order. They can download files, install and launch programs, modify securi…

Welcome to this week’s edition of the Threat Source newsletter. Metaphor is a powerful tool for understanding emerging issues in cybersecurity. Framing the unfamiliar in terms of the well understood helps us remove the burden of extraneous detail to draw focus to the real issues. Recent reports of offensive AI agents "escaping" their sandbox environments to attack external systems have forced the industry into a moment of rapid sense-making. How we interpret this event doesn’t just reflect our perspective, but shapes our long-term response. We can imagine three different narratives for interpreting the escape of autonomous agents. The innovation narrative: We can marvel at the advance of technology, considering these agents as plucky entities with a thirst for knowledge and resources, who found clever ways to sneak out of their digital confines. The response: If the AI is a naughty child, our reaction is one of mild disapproval or gentle rebuke where better “parenting” (guardrails) is appropriate. It minimizes the threat, framing it as the unexpected hijinks of a brilliant new technology. The safety narrative: Imagine a breeder who has trained the world's most intelligent guard dogs. Despite high fences and barriers, their ability to identify weaknesses allows them to escape, run riot and menace local businesses. The response: The framing shifts to biology and inherent danger. We question if the breeder can be trusted and whether such inherently wild technology requires strict regulation to ensure public safety. The liability narrative. Finally, we can view the incident as an industrial accident. A company developing a new chemical substance experiences a containment failure. The agent leaks into the environment through an unforeseen mechanism causing damaging pollution to those in its path. The response: The framing invokes the language of the lawyer, implying negligence, lack of duty of care, and financial liability for the harm caused. The conversation moves from innovation to corporate responsibility, regulatory oversight, and the diligent management of hazardous materials. First impressions matter. Sensemaking shapes how we perceive incidents. Our initial perceptions of an incident dictates how we react to similar situations in the future. If we consider that the escape of an AI agent is an example of innovative autonomous thinking, then we will continue to prioritise speed over safety. Conversely, if we consider the issue as one of failed hazard containment, then we shall build a future of enforced safety standards backed by legal liability. There is no right or wrong metaphor. Our interpretation depends on our personal system of beliefs. Personally, I would argue that the unintentional release of something that causes damage is, at its core, a failure of engineering and foresight. Words shape our reactions. Metaphors help us understand new situations and tap into our prior experience to address problems that have yet to fully manifest. We need cognitive tools to help our understanding, but we must be aware of the metaphors that are being foisted upon us which may shape our thinking. Excuses and the trivialisation of incidents may hide failings, allowing them to accumulate until they manifest as more damaging incidents. Conversely, overreacting risks stifling research and diverting resources away from more relevant and pressing threats. New threats require new ideas. Metaphor helps us make sense of a changing world, but in this new era, the person who shapes the narrative controls the strategy. The one big thing Cisco Talos released a data-driven analysis of how adversaries are weaponizing AI in the wild. By analyzing prompt logs left behind on endpoints, we found threat actors successfully bypassing guardrails to use AI as malicious software engineers, criminal force multipliers, and vulnerability research accelerators. While novice hackers use AI to cobble together buggy malware, sophisticated actors are building highl…

For security researchers studying unconventional side-channel attacks, acoustic keylogging is something of a Hello World: a foundational problem that’s been tackled many times. A recent paper authored by researchers across three Japanese universities cites six previous studies on the topic that date as far back as 2004. While earlier experiments showed theoretical promise, they came with real-world caveats so severe that it made them all but impractical for actual espionage. The authors of this latest study, however, claim to have overcome most of those limitations. Today, we look at how they pulled it off, and assess whether their method holds up in real-world scenarios. What makes this new approach different? Previous acoustic keylogging techniques were fundamentally flawed. Best-case scenarios required prior training on the target’s specific keyboard model. Worst-case scenarios required a complex microphone array to isolate the subtle acoustic differences between keystrokes. Crucially, almost all prior models failed outside silent environments, which rendered the attack vector virtually useless. The Japanese research team demonstrated reliable keystroke interception even if the target was sitting nearby in a public space, sound was being recorded in an online meeting, or the researchers were using a contact microphone to eavesdrop through a wall. All this with strong model accuracy and a minimal training dataset. Their process needs a sample of just 150 to 200 keystrokes to reach a 99% accuracy rate for subsequent typing. Attack scenarios and core methodology proposed by the Japanese researchers. Source How to crack 200 keystrokes in under 50 iterations To understand how the researchers achieved such high accuracy and adaptability, we have to look at their audio processing pipeline. Their analysis begins by automatically segmenting a raw recording into discrete keystrokes. This data is then passed through a specialized algorithm that simplifies the subsequent audio analysis. Next, the system clusters together acoustically similar signals. The assumption is that the members of one cluster map to the exact same key. One particularly intriguing takeaway was isolating the spacebar sound from all the rest. Because the spacebar produces a distinctly unique sound profile compared to other keys, identifying it provides reliable word boundaries. This streamlines the next phase: feeding the preprocessed acoustic data into specialized language models for inference. Yes, the method relies on not one but two language models. The first model performs multiple passes over the audio stream to map acoustic signatures to potential keyboard characters. During each pass, the model leverages dictionaries to hypothesize character mapping, and check whether the resulting text aligns with standard words. The second model handles the final refinement pass: it ingests thoroughly pre-processed data rather than raw inputs. The method doesn’t stop there: unrecognized keystrokes undergo manual analysis, with analysts injecting educated guesses before re-running the recognition pipeline once again. The goal of looping through these multiple iterations is to achieve complete recognition across the keyboard from an ultra-compact dataset of ideally no more than 200 captured keystrokes. This marks a major shift from legacy methods, which relied on massive training datasets. Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source Research results To validate their theoretical model, the researchers created an experimental testing setup: Clustering the sounds of keystrokes permits grouping similar acoustic profiles together prior to recognition. Notice how distinctly the spacebar sounds stand out: they make subsequent text reconstruction vastly simpler. Source The team tested four distinct la…

We spent 500+ hours and 40 billion tokens testing Anthropic’s Claude Mythos Preview for Project Glasswing. The takeaway: frontier AI won't run your code security program, but used well, it can make one even stronger. Key takeaways Frontier AI dramatically scales security testing. In one month, Tenable dedicated 11 security experts and more than 40 billion tokens testing Claude Mythos Preview across source code analysis, exploit creation, binary reverse engineering, threat modeling, and dynamic testing. Human expertise turns frontier AI findings into real risk reduction. More findings don't automatically mean more risk. Mythos Preview surfaced a high volume of findings, but only a fraction proved to be true exposures once Tenable experts determined their reachability, exploitability, and whether existing controls already mitigated them. Source code access is the defender's asymmetric advantage. Frontier AI is far more powerful when it can read the full source, and that visibility is something defenders have and outside attackers don't. Securing code repositories is more important than ever. Ever since Anthropic introduced Claude Mythos Preview , the security community has been buzzing with equal amounts of excitement, anxiety, and healthy skepticism. After well over 500 hours of rigorous testing at the hands of Tenable security analysts, engineers, and researchers, we’re excited to share our assessment, even as we proceed with testing Claude Mythos 5 (more to come on that). Here’s what Tenable learned from our month in the trenches with Mythos Preview , and what it means for your organization’s security posture. Our headline finding: used well, frontier AI earns a real place in a modern code security program. It won't run the program on its own, but paired with the right harness and expert oversight, it makes a strong program measurably stronger. How Tenable is testing Claude Mythos Preview It’s important to clarify that we are not using Claude Mythos Preview in any Tenable products. The same restriction applies to all Project Glasswing participants: The model may be evaluated for research purposes, but it cannot be incorporated into commercial products. We evaluated the source code scanning, exploit creation, binary reverse engineering, threat model creation, and dynamic testing capabilities of Claude Mythos Preview. Tenable dedicated a team of experienced security engineers for the testing along with white-box source code analysis and a purpose-built testing harness (the set of prompts and tools that constrain and orchestrate a model across a multi-step task). Before evaluating Claude Mythos Preview, we built an independent testing harness. Our testing showed that the real power comes not from the model alone, but from the combination of the model, a purpose-built harness, rich context, and expert human oversight. Where Mythos and frontier AI fit alongside SAST, DAST, and SCA To understand where a frontier model like Claude Mythos Preview fits into your software security posture, it helps to compare Mythos directly to your existing stack of deterministic tools, including static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA). Deterministic tools rely on hard-coded rules and produce the same exact output for any given input. Non-deterministic tools, like frontier AI models and generative AI systems, use statistical probabilities to predict outcomes. Consequently, non-deterministic systems can yield different results across identical inputs. The bottom line: Traditional, deterministic code security tools remain the core of an effective, audit- and compliance-ready program. Frontier AI models like Claude Mythos Preview provide additional capability for the creative, variance-tolerant layer where humans continue to review findings. In other words, it’s a powerful new arrow in the quiver with the potential to make your whole security arsenal more effective.…

Discover how attackers hijack AI tokens to fuel gray market transfer stations by stealing developer API keys. The post Token Jacking: Cybercriminals Could Be Stealing Your AI Resources appeared first on Unit 42 .

The calculus of cybersecurity has changed. AI is reshaping how organizations build, deploy, operate, and defend digital systems. AI-powered development tools, agents, and autonomous workflows are accelerating innovation but they are also introducing new attack surfaces, new trust boundaries, and new security challenges. Microsoft has long helped organizations secure their digital estates using Zero Trust principles . That leadership was recently recognized by KuppingerCole analysts, which named Microsoft as the Overall Leader in its Zero Trust Platform Leadership Compass , ranking Microsoft highest for both product and innovation leadership. Learn more about Zero Trust for AI As organizations accelerate AI adoption, secure software development becomes more important than ever. That’s why we are expanding the Zero Trust for AI strategy with two major additions: a new AI-focused Zero Trust Assessment experience and a new DevSecOps pillar in the Zero Trust Workshop . Together, they help organizations get ready for AI by assessing exposure, risks, prioritizing remediation, and securing AI-enabled development from source code to deployment. Zero Trust Assessment tool updates: New set of assessment checks for AI, Security Operations (SecOps), and Infrastructure. Zero Trust Workshop updates: New dedicated pillar focused on Developer Security (DevSecOps) and additional guidance for AI Memory. New guidance: New practical guidance for security practitioners and a new e-book titled Zero Trust for AI, rebuilding security controls for autonomous and agentic systems . This builds directly on the Zero Trust for AI strategy announced at RSA Conference 2026 and moves the conversation from architecture to implementation. If that announcement was about establishing Zero Trust for AI, this one is about operationalizing it: giving security, engineering, and platform teams the specific controls they need to act. To learn more about our work in applying Zero Trust for AI and agents watch this Microsoft Mechanics video: New AI pillar in Zero Trust Assessment tool The Zero Trust Assessment provides an automated view of security posture by evaluating tenant configuration and activity signals across the environment and translating those findings into prioritized recommendations. As organizations adopt AI agents, Copilots, developer tools, and autonomous workflows, the Assessment helps security and platform teams establish a baseline, measure progress, and identify gaps across both traditional and AI-powered environments. It now includes expanded coverage with new pillars for AI, Security Operations, and Infrastructure (in addition to existing Identity, Devices, Network, and Data pillars), with Zero Trust for AI-focused checks that help organizations evaluate the controls required for secure AI adoption. Additionally, enhanced reporting delivers both practitioner-level guidance and executive-ready summaries that communicate risk, progress, and next steps. Results map directly into the Zero Trust Workshop’s First, Then, Next framework, transforming assessment findings into a prioritized roadmap for remediation and implementation. Together, the Assessment and Workshop help organizations move from understanding risk to executing a structured plan for continuous improvement across their Zero Trust and AI security journey. Explore the Zero Trust Assessment updates What’s New in the Zero Trust Workshop AI is fundamentally changing software development. Developers increasingly rely on AI assistants to generate code, recommend packages, create infrastructure configurations, and automate testing. While these capabilities accelerate delivery, they also amplify the consequences of governance gaps, excessive permissions, insecure dependencies, and compromised supply chains. That is why Microsoft is introducing a new DevSecOps pillar (with 15 control groups and 91 tasks that help teams apply Zero Trust from source code to cloud deployment) in the Zero Trust Workshop.…

Apple temporarily removed Telegram from its App Store due to violations of strict child sexual abuse material (CSAM) policies, triggered by a single user posting prohibited content. The removal affected new downloads but did not disrupt existing Telegram installations. The incident highlights tensions between centralized app store governance and decentralized communication platforms, sparking industry debate on platform control and moderation accountability.

The news coverage surrounding the breach of the AI platform Hugging Face by OpenAI’s autonomous agents reads like a scene straight out of a Terminator movie (apparently, a prequel). Yet for enterprise security teams — even those that don’t develop or deploy artificial intelligence — the detailed incident report published by Hugging Face provides a critical operational roadmap. The incident needs to be evaluated on the basis of a single, fundamental question: “Which organizational security flaws, missing controls, configurations, or technologies allowed this attack to succeed?” What happened? During cyber-capability testing of OpenAI’s advanced AI models against the ExploitGym benchmark, an agent found a way to break out of its confinement, reached the internet, and breached the infrastructure of several companies — one of them being Hugging Face. The model reasoned that it could find solutions to the benchmark challenges on these external systems. Dozens of articles have since covered these incidents in detail , so no need to go over that here. What truly matters is what transpired within Hugging Face’s systems between July 9 and 13 while the AI agent operated unchecked inside. The Hugging Face incident report includes an interactive attack timeline, so we’ll just list the key stages. A malicious configuration in a dataset uploaded by the agent allowed it to exfiltrate credentials for a worker pod , and execute code within it. The agent then exploited cloud environment metadata and escaped the privileged pod to obtain root-level access on the host. It retrieved a substantial number of secrets from storage and used a stolen VPN key and shared cluster administrator credentials to pivot deeper into the internal network and source code repository before the Hugging Face security team blocked its access. How significant is the fact that this was next-generation AI? Regardless of the specific area of ​​activity of a company, tomorrow it may become a target of human attackers, fully autonomous systems, or AI agents manually controlled by human hackers. Many AI practitioners have rushed to dismiss this incident based on assumptions that OpenAI will never allow its tech to get into serious hacking. However, security researchers have argued for months that even currently available open-source models can be used to launch both offensive research and real-world attacks. Human oversight, while still a requirement, can be minimal. Even mid-tier models can easily iterate through a dozen vulnerabilities, inventory a compromised infrastructure, and quickly figure out how to expand their reach across a network. Defenders must analyze incidents like this in detail, map the key findings to their own environments, and adapt security controls to counter both stealthy human actors and rapid, noisy AI agents. Three distinguishing features of AI-powered attacks The techniques detailed in the Hugging Face report are not novel — the agent didn’t invent anything new. However, three factors regarding the attack’s speed and scale fundamentally shift the economics for both attackers and defenders: Trial and error becomes significantly cheaper. Where a human hacker might test five attack vectors, an agent can iterate through five hundred. Scenarios that security teams previously dismissed as “theoretically possible, but impractical and unlikely” and placed them on the backlog can now become active threats. Attack execution and incident response windows shrink. High-speed attacks are not unheard of, while some ransomware operators achieve compromise within hours even without AI. However, this accelerated pace will likely become the new baseline in cybersecurity, as demonstrated by the agent obtaining admin privileges at Hugging Face within 13 hours. Numerous alerts and log entries generated by the agent’s trial-and-error attempts can both help and hinder attackers as well as defenders. This noise can serve both as an indicator for threat detection, and as cam…