We're all petrified about missing a critical event or misclassifying an alert, but when we're talking about incident response (IR), there are often hundreds if not thousands of alerts to parse through. It's easy to get caught up with one alert because it feels "too hot" or maybe not spend enough time looking into something that initially seems "too cold."
Welcome to Issue #162 of Detection Engineering Weekly!
Every week, I read, watch and listen to all the Detection Engineering content so you can consume it all in 10 minutes. Subscribe and get a weekly digest of the latest and greatest in threat detection engineering!
✍️ Musings from the life of Zack:
I had an excellent long weekend celebrating the 4th of July here in the U.S.! It was a good lead-up to a rather disappointing World Cup loss on Monday :(
We recently bought a kids’ WiFi landline phone thing so they can call family and chit-chat. Let me tell you: it’s been terrible. The quality/service is poor, and it just feels cheap. So, I’m trying my hand at rolling my own FreePBX server with an upstream trunk provider. I haven’t been this excited about a project in a long time :D will report back once I get it deployed
Detection & Response Happy Hour @ Black Hat
If you are going to be in Vegas during Black Hat, come swing by Tom’s Watch Bar @ the NYNY Casino right on the strip on Tuesday!
I’m running it back after BSides/SFRSA with friends and supporters of the newsletter, Cotool.ai. It was super chill at RSA with no vendor b.s., so escape Mandalay Bay and come talk shop with other practitioners.
This blog is one of the single best deep dives on detection validation I’ve seen in years. It hits a bunch of themes, including types of detection testing and architectural decisions for building and deploying end-to-end tests, and clearly describes an elegant and repeatable agentic loop for this use case. Let’s break it down piece by piece, because there’s a ton here and I highly recommend reading this one if you don’t want to touch any other story in this issue.
Types of detection testing illustrated in the blog
Security folks tend to stealconcepts from SRE and developers and relabel them with fancier, cooler names, but the underlying principles remain the same. The picture above from Kongsgård shows how we celebrate concepts like testing and “chaos engineering” in security and map them to the security telemetry lifecycle. Regression tests, for example, focus only on verifying that an input (telemetry) produces an output (alert). Synthetic ingestion is an integration test of the ingestion and shaping of telemetry to generate an alert. End-to-end testing looks at the full telemetry → detection → response pipeline.
My favorite themes
Since this post has enough content to fill several posts, I’m going to point out two of my favorite themes so it doesn’t feel like I’m repeating or rewriting Kongsgård’s content.
Lab environment and TTP framework
Booting labs up to run simulations takes a ton of time and effort, especially if you are starting from scratch. The goal is to replicate your environment as closely as possible, but there are always trade-offs in simulation. Some of these tradeoffs include:
Environment mirroring: A host running in a VPC can help mirror what your endpoints or cloud resources look like, but it won’t be exact. Detonating potentially dangerous tooling inside a production environment can introduce externalities or even real security incidents if you aren’t careful
Baseline activity: A user, much like an agent, is non-deterministic. The telemetry they generate from normal activity is just as important to model as the malicious traffic itself
Provisioning discipline: Running a small amount of Atomic or Stratus Red Team tests is manageable from an individual detection engineer’s perspective. If you want to run your whole catalog of detections, you need to start thinking like an SRE or software engineer, as you’ll hit scaling and drift issues with your infrastructure
Kongsgård’s detonation environment has a high level of discipline to address these tradeoffs. The section on the Lab environment uses several DevOps paradigms, such as golden images, configuration management, and deployments via GitHub Action runners. Under the hood, they use Meta’s TTPForge as their adversary simulation framework for execution on detonation hosts. It offers a content-rich, multi-step attack-generation feature set that is adaptable to their agent-harness framework.
Agents as validation drivers and the schema knowledge base
Singular prompt, one-shot agents have their place in implementing agentic systems in security, but they tend to perform poorly as tasks become more heterogeneous. Since the task is end-to-end detection validation with TTP generation, rule tuning, and detonation, a Claude Code or Codex agent would not be sufficient.
The harness is the differentiator for anything agentic security. Remember that! Their agent isn’t improvising an attack; it’s following a plugin that teaches it to write TTPForge YAML files, ship them over SSH to a lab host, run the detonations, and then queries Splunk to see whether telemetry arrived and the detection matched. When a step fails, lifecycle hooks block progress until the agent finds the issue. Here’s the high-level architecture:
My recommendation to all my readers is to design your agentic workflows around single agents that do one thing very well. It’s as if you are extracting one piece of expertise from your brain and encoding it into a prompt to do a single thing. In this particular case, Kongsgård designed two plugins to perform discrete tasks.
detectionkit builds the TTP definition via TTPForge, writes the detonation test, deploys and runs the detonation. Its singular purpose is to replicate threat actor activity in a common & repeatable lexicon
splunk is the plugin that performs the validation that the correct telemetry was captured, the search for the rule was performed quickly, and continuously discovers index structure to understand rule performance and drift
Security vendors who sell agentic capabilities typically don’t expose their harnesses at the level of detail shown in this blog. Researchers and open-source enthusiasts are quickly catching up with these vendor-led harnesses, and this truly gives detection teams agency to choose between build and buy.
It’s been easier to admit that I can’t imagine a world without a Claude Code or Codex. The prompt was the star of the show for the first year or so of this coding agent frenzy, but it’s now squarely the quality of the harness that brings detection to the next level.
This is a Part 2 post from Byford’s previously featured work on writing rules for Anthropic’s Compliance API. The cool part here is that, unlike his last post, which focused on the prompt content itself, this looks at the execution layer of the coding agents. I’ve always interpreted the execution layer as how the agent interacts with the filesystem itself. This presents unique detection challenges because, in my opinion, the impact is the same, such as downloading and executing a binary, but the paths are different, such as malicious skills, reading a malicious prompt, or loading a malicious plugin.
Byford splits the threat categories into five distinct buckets: excessive agency and permissions, supply chain threats, dangerous actions, sensitive information disclosure, and data poisoning. The architecture is clever where the Compliance API is used as an enrichment backdrop during investigations, so you can combine unstructured data from prompts with the structured data generated from Claude hooks:
And here’s the enrichment layer after a SIEM rule fires from the OTel collector:
Much like detection engineers have had to become supply chain security experts in the last two years, I don’t see a world where we also must become AI Coding agent experts in the next year or so. I never considered using prompt and response content generated from coding agents in the Compliance API as additional context for SIEM alerts, so I’m now going to steal that idea and see what I can do at my day job (sorry, Andrew!).
Evaluation datasets are critical for understanding model performance. Much like in my analysis of this week’s Gem, one-shot prompts can perform well under very constrained conditions, but without something to measure real-world malicious vs. real-world benign, you should limit your confidence in virtually all agentic security applications.
I found this SOCBench website & corresponding open-source repository, and it reminded me of Cotool’s Research benchmarks with similar datasets. This specific one includes a NetFlow dataset containing both malicious and benign network traffic. It’s also a bit more opinionated about persona benchmarks, ranging from SOC analyst to detection engineer, and includes more architecture, with tool catalogs and playbooks for those personas. Their first benchmark around detecting maliciousness:
Anthropic performed the best but it looks like it cost the most. I find it interesting that OpenAI’s benchmark had the threat analyst perform the best vs the SOC analyst in the other two.
Two issues ago, I linked a blog by Aman Khurana that helped demystify the peculiar supply chain architecture behind VSCode extensions. The big takeaway I took from that blog is that not surprisingly, the more security engineers dig into supply chain security, the more they realize how difficult it is to piece together OSS ecosystems to perform effective detection and blocking. Coding agents are built to be autonomous and extensible, just like OSS. The difference lies in the non-deterministic way these agents perform coding tasks, due to intentionally designed boundaries.
In this post, Nesbitt unveils his threat model around coding agent skills. A skill is a bundle of prompts, code, dependencies, and tool permissions. Anytime a skill is used, the skill prompt is injected into the context window, and a set of tools and scripts gets exposed to the coding agent. The more frightening part of the Skills supply chain security is that, instead of a single npm command installing other packages in Node that eventually land a piece of malware, you can have a Skill install packages from virtually any ecosystem, and sometimes those packages are just more prompts.
I don’t think we are truly ready for a large-scale malicious Skill campaign, much like what we’ve seen with the likes of TeamPCP. Nesbitt points out several issues with how Skills are installed, deployed, and managed, and it certainly seems that this ecosystem is in the same stage that npm was in several years ago.
The DoJ nabbed another residential proxy platform linked to the Popa Botnet. Krebs post here helps aggregate some of the data published by researchers at Google, Lumen and Spur. The wild part to me is that this proxy platform is linked to an Israeli company, and I’ve always assumed that these networks are owned by non-Western firms who are harder to work with outside of the U.S.’ sphere of influence.
These types of botnets finally figured out how to monetize without DDoSing. Krebs referenced research from Spur that nearly 50% of TV Apps on the LG Smart TV platform add the TV to these botnets, which are then sold as residential proxies.
Super interesting write-up from a developer who encountered a Contagious Interview-style backdoor in their Tailwind configuration. They never figured out how it got there, but the indicators are classic Contagious Interview:
Targeting developers and backdooring their code
C2 server communication to an immutable blockchain style API
Rewriting git history to conceal the compromise
It didn’t necessarily say what the impact was or whether the campaign resulted in data exfiltration. They did find several unknown processes running in their production environment, so likely something happened there. If I had to guess, it was a PwnRequest due to the rewriting of the git history, but that’s about as far as I’ll go before I start placing bets.
This is a cool Linux backdoor writeup of a piece of malware that, based on my ~limited research, targets a Chinese-focused router typically deployed to East Asian/Chinese businesses. It’s an ELF binary that impersonates OpenWRT’s libjson_script.so.0. It was hard to ascertain at first, but it certainly is not a shared library and runs in userland. I don’t necessarily know whether the victimology is Chinese firms, which could make this a Western-based piece of malware, but it seems compact and very specifically designed for one router brand, which makes it smell like an APT implant.
The TALOS research team uncovered an offshoot of EvilTokens, a device-code phishing-as-a-service kit. Kelley uncovers the initial BEC-style lure and then reverse-engineers the kit to find modern front-end components, such as single-page application lures, and a full backend dashboard written in React. Some of the differentiating features of ARToken Kelley found include keyword searching across victim mailboxes, post-exploitation tooling against victim SharePoint servers, and even collaborative session links for operators working on the same ARToken deployment server.
Lydia Graslie’s Gem from last week helps protect against some of these attacks, especially if you monitor which Microsoft management surfaces emit audit data for device‑code flows and token lifecycles, and treat gaps and schema shifts as first‑class detection problems.
SOCBench’s open-source harness for evaluating alert datasets. The current dataset only contains NetFlow telemetry, but it looks like they want to add more. Their harness is the most interesting between playbooks, personas and how they run the evals themselves.
EasyTokens is a device code phishing toolset that emulates device code phishing as a service kit like EvilTokens. This one is more focused on performing the device code phishing attack itself, so you can use this to pivot into cloud and M365 environments.
Nox is an open-source attack surface scanning tool. There are 300 module plugins across 24 different categories. You can run each module individually or run a full scan that steps across all 24 categories to find everything from exposed credentials, vulnerabilities and OSINT findings.
Shortdot is a registry operator that hosts seven top-level zones (TLDs) that, according to PhishDestroy, almost exclusively contain fraud, phishing, and malware websites. This repository holds all of their research, enumerating the zones and their phishing website analysis across the seven sketchy-looking TLDs. Their research also includes financial research and how Shortdot charges ICANN fees to operate these zones.
Every week, I read, watch and listen to all the Detection Engineering content so you can consume it all in 10 minutes. Subscribe and get a weekly digest of the latest and greatest in threat detection engineering!
You've watched the demos. Whether it's Claude Cowork, ChatGPT Enterprise, GitHub Copilot, Cursor, or internally developed agents, AI systems are no longer answering questions. They are connecting to enterprise data, invoking tools, making decisions, and executing multi-step workflows across applications without human intervention. The capability is real, and organizations are rapidly moving from experimentation to deployment.
Teams are no longer asking if they should use this, they have accepted agentic tools as the reality. But the board and the infosec team are asking a different question: can this capability be secured and controlled at enterprise scale? Can security teams prevent sensitive company data from being exchanged without oversight?
Anthropic built meaningful access controls into Cowork — role-based permissions, group spend limits, usage analytics and connector restrictions — so the answer is a qualified yes. Those controls handle who can use the tool and what they can connect to, but they don't answer whether a specific action inside a given session is safe. That gap is the one standing between a successful pilot and a successful org-wide rollout.
The Gap That a Demo Doesn’t Expose
The organization’s admin assigns roles, sets spend ceilings per user group and restricts which connectors have access to write to your database. Anthropic's OpenTelemetry support even lets your team pipe session events into your SIEM. These controls cover real ground, but they operate at the permissions level — answering whether a person is authorized to use the tool rather than whether what's happening inside a session is safe.
Consider what that gap looks like in practice. Let’s consider two scenarios. Your finance analyst has full Cowork access and uploads a quarterly forecast containing unannounced acquisition figures. The access controls confirm she is authorized to use the tool, but nothing evaluates whether that information should be exposed to a model. That's an AI data loss prevention risk, and access controls are blind to it.
The risk becomes greater when agents move beyond information retrieval and begin taking actions. Let’s say a scheduled Cowork automation is set up to pull weekly competitor pricing from the web. A target site embeds hidden instructions in its page content. The agent, running unattended, reads them as legitimate commands and begins modifying local files and triggering actions your team never authorized. By the time anyone notices, the agent has already acted.
The first scenario exposes a governance problem because your security team has no visibility into what data is flowing through AI tools across the organization. The second is a runtime security problem as there is nothing evaluating whether an action in progress is safe, regardless of whether the user was authorized to start it. Neither gap is addressed with the predefined controls in Cowork; both need to be solved before you can say yes to Cowork adoption in the whole organization.
Why Traditional Controls Break Down
Traditional enterprise software behaves predictably. Access controls work because administrators can reasonably anticipate what an authorized user or application will do once access is granted.
AI systems operate differently. Agents combine models, tools, data sources, and reasoning paths dynamically at runtime. An authorized user may start with a simple request, but the resulting chain of actions may evolve in ways that were never explicitly programmed or anticipated. The challenge is no longer controlling who can access a system. The challenge is securing and governing what happens after access has been granted.
The Missing Layer is Runtime Security
Anthropic's access controls establish who can use Cowork and what they can connect to. But as the examples above show, they don't protect against what happens inside a session: a finance analyst uploading sensitive acquisition data to the model, or a scheduled automation being hijacked by a malicious instruction embedded in a webpage it was directed to visit. What organizations working with Cowork need is a layer that enforces data and security controls and gives complete visibility at runtime across all Cowork agents in the enterprise every interaction boundary.
An AI runtime security layer that sits between your teams and the model providers such as Anthropic, AWS Bedrock, Google Vertex or any combination, and evaluates risk in every interaction. It inspects every request, every tool call and detects sensitive data like client names, financial projections, internal pricing and contract terms. It enforces agent identity controls, so every automated action is traceable to a specific workflow and owner.
Your CISO gets the audit trail and your Infosec team gets the evidence.
The AI Enterprise Needs a Control Plane
The CIO needs the observability for all Cowork activity and costs. An AI control plane allows the CIO to set spending limits per team and use case across every AI tool from a single console. Procurement asks for a quarterly forecast across all AI spend, and you pull it from one place instead of aggregating reports from four different vendor dashboards. If you need to move providers for cost or compliance, the gateway reroutes traffic without disrupting your teams or breaking your workflows.
Claude Cowork may be where organizations begin scaling their AI journey, but it won't be the only AI tool your teams use. Developers will use coding assistants, business teams will leverage the AI built into SaaS applications and data science teams will deploy custom agents for their workflows. New models, new providers and new workflows will continue to appear.
The challenge isn't just governing one AI application; it’s governing AI activity across the entire AI enterprise.
Everyone looks to secure each tool individually: configure Cowork's controls, configure your coding assistant's controls, configure your internal agents separately. But this approach doesn't scale.This is the sole purpose of the control plane. It sits above individual tools, applications and models and enforces security policies, across every AI interaction.
Prisma AIRS AI Gateway provides that centralised control plane. Organizations that deploy Cowork behind our gateway get runtime security, data protection, agent identity controls, and full visibility, applied consistently, without changing how teams use the tool. The same gateway secures every other AI tool in your environment on the same terms.
Cowork may be where the journey begins, the gateway is what allows it to scale and secure the AI Enterprise.
As artificial intelligence (AI) adoption surges and organisations move from the ‘should we?’ phase to the ‘how do we?’ phase, it’s natural to evaluate the likelihood of positive returns on AI investments. That’s always been the case with the onset of each new technology paradigm: C-suite executives, guided by their boards and aided by technical and business teams, remain keenly focused on traditional metrics such as return on investment, shareholder equity, developing and extending competitive advantage, and ensuring superior customer relationships.
This time is different, however. I recently experienced that firsthand when I went to visit a major customer. My contact, a senior decision maker, gave me a pointed piece of advice about how to talk about AI with his boss, the CEO: “Please don’t say anything negative about AI.” The subtext was clear: The company was fully committed to AI and didn’t want any cognitive dissonance to dissuade them from their mission.
It's hard to imagine a CEO taking such an absolutist stance on previous technology waves, such as cloud, bring your own device, or the internet of things. CEOs, board members, and technical leaders would be pragmatic in evaluating the benefits of investments and put mileposts in place to gauge progress – and to determine if and how to proceed.
AI is certainly a different kind of paradigm, though. While no one is casting aside careful evaluation and monitoring of AI investments, the underlying assumption is that we’re stepping on the accelerator. We’re all enthused not only by its potential for transformation and innovation, but also by how this technology can be leveraged for remarkable societal good.
However, while the accelerating momentum toward AI and agentic systems is undeniable, it is vitally important to set aside the fervour around AI and take a sober look at how to deliver safe, secure, and tightly governed systems at enterprise scale.
Many organisations are underestimating the challenges of AI governance, in large part because they think they’ve been here before. They already have many experiences of ensuring robust cybersecurity and strict governance for new technologies, as they’ve done for remote systems, cloud computing, the internet of things, and more. They already have a corporate commitment to doing governance correctly and a sound governance model.
But this new era of AI and agentic systems is different. New challenges abound, and AI strategy, build-out, and governance must be in alignment from the start to ensure proper operational, ethical, and regulatory outcomes.
Our intention with this Peer Insights guide is to raise what we believe are existential issues around governance for this powerful, complex, and unprecedented technology wave. Few technologies have merited the often overused phrase ‘inflection point’ more than AI. The speed of AI adoption is nothing short of breathtaking; however, today’s runaway embrace of AI is far stronger than our current ability to govern it. That’s because AI represents a fundamental shift in how organisations do their business, interact with customers, make vital decisions, and execute their plans. This isn’t just a technology play: It’s a strategy for success and survival for entire industries and our global economy. The stakes have never been higher.
CEOs care so passionately about AI because they see it changing nearly everything we’ve learned and believed to be true about organisational success and failure. CEOs are in their positions for one purpose: to grow the business. AI can do that by transforming their processes and sparking new ideas. When that customer representative forewarned me, I really wasn’t surprised to hear his CEO felt so strongly about AI: Research from BCG indicates that more than 94% of CEOs say they still plan to deploy AI irrespective of demonstrated business value, even if there is a lack of tangible ROI or financial benefits from the start.
Which brings us to the central role of AI governance. As we all know, there are many fundamental elements to any governance strategy, starting with robust, scalable, and intelligent cybersecurity. Cybersecurity - the foundation of governance - also includes the twin imperatives of accountability (‘rogue AI’ being a real thing, after all) and regulatory compliance.
But good AI governance has to go even further. Operational integrity is key to good governance because so much sensitive and even proprietary data is poured into AI models and accessed through powerful agentic AI systems. Now more than ever, organisations have to be transparent with customers and trading partners about how their AI systems operate, what kind of data is accessed, and how it is protected. And that doesn’t just mean being upfront with customers by telling them when they are interacting with an AI agent. Let’s take a typical retail use case: Imagine you’re on a website looking at clothing, and the agent recommends specific styles of clothing in specific colours. True operational integrity would allow you to discover why and when the agent made those recommendations. Was it based on your prior purchasing history, or on your browsing patterns on a recent web session? AI and agentic governance take the guesswork out of the equation for those interacting with the system and help breed greater confidence and trust.
It's critically important for decision makers to view AI governance holistically, rather than through a series of narrow lenses. For instance, even though cybersecurity is the foundation of good AI governance, it’s a mistake to treat AI governance primarily as a cybersecurity problem. If asked about ownership of AI governance, CEOs cannot and should not reply, “Oh yeah, the CISO has that covered.”
AI governance is fundamentally an enterprise risk problem, which means everyone must be involved in creating, deploying, managing, evaluating, and adjusting AI governance guardrails on a real-time basis. Again, AI is a different kind of risk environment than any we’ve previously encountered. For the most part, organisations are simply not adequately prepared to apply the right level and right type of governance to AI and agentic systems. I’ve spent much of the past 15 years of my career building governance frameworks, and while it has never been easy, we have had the advantage of being able to control many of the variables – such as infrastructure and network access – impacting governance decisions. With AI and agentic, we no longer have that advantage.
To explore the critical and complex issues of AI governance, we’ve enlisted five leading voices to bring their real-world experience to the discussion. Together, our five authors help lay out the new rules of the road for governing AI and agentic systems at scale.
Just as my customer gave me a heads up about the realities of speaking with his boss about AI, I’d like to offer you a heads up about the realities of AI governance challenges before you read this Peer Insights guide.
Visibility is paramount for successful AI governance. As we learned during the growth of trends such as cloud, bring your own device, and remote work, our employees will push the envelope with a do-it-yourself mindset. These tech-savvy and resourceful users are already making rogue AI a reality, so organisations need more visibility than ever into where AI ‘science projects’ and sandboxes are operating without anyone’s knowledge.
AI governance must reflect the stunning velocity of change in AI development and deployment. Not only does AI have its own never-imagined rate of change, but the technology is changing everything else faster – product development, supply chains, marketing programmes, and more. AI governance has to evolve just as rapidly. Governance in the AI world must be a living system, constantly evolving with new technology use cases.
Trust boundaries are incredibly different and difficult to manage in AI governance. AI represents a new class of identity that simply didn’t exist before. That means AI doesn’t fit neatly into your existing identity management framework, making things like application whitelists and zero trust network access less effective.
Unfortunately, many CEOs, board members, and business executives simply don’t understand the profound importance and complexity of these issues. They may have been heartened by how they integrated generative AI into their technology frameworks and their business processes, but GenAI was pretty familiar territory for CIOs, CTOs, and CISOs. Agentic AI is different for several reasons, including its automation and self-learning capabilities. Don’t be lulled into a false sense of security: Agentic AI is not simply a refresh of GenAI.
As you get ready to dive into the following chapters, rethink how you define governance when applying it to AI systems and agentic AI. Most traditional governance models are imagined, constructed, and deployed as gates, preventing people from doing things or going places they shouldn’t. Instead, think of AI governance as a guardrail to guide and direct people to get the most out of AI without creating problems. With so much excitement and investment around AI, organisations – and their employees – want to get the most out of their AI and agentic systems. We all know people don’t want to hear “no, you can’t do that”, so an effective governance system should use guardrails to drive proper, responsible, and safe usage of the technology.
Finally, as complex as AI and agentic governance are and will continue to be, don’t overthink things in hopes of creating the perfect model – it doesn’t exist. My advice is to start now, even if the model and framework are imperfect, and then bring the business along with you.
We at Palo Alto Networks are excited to give you insights, ideas, and actions you can take away from the chapters of this guide. We encourage you to share what you learn with your colleagues, peers, and team members – and to take prudent steps to build an AI governance model that rewards innovation without allowing your organisation to drift into dangerous waters.
Vulnerable and outdated software components are one of the most common issues encountered by BHIS during web application penetration tests. The vast majority of web applications use third-party components such as jQuery, Angular, Bootstrap, or countless other libraries.
Welcome to Issue #161 of Detection Engineering Weekly!
✍️ Musings from the life of Zack:
I had an excellent vacation at the beach with my family! We stayed at an Airbnb with a 1-minute walk to the ocean. There’s something about the crashing waves and the smell of salty water that makes me wish I could afford a house there D:
I am locked in & going to BSides LV/BlackHat/DEFCON! I’ll be posting details for my Detection & Response Happy Hour next week with sign-ups. Mark your calendars for Tuesday, Aug 4 at 5 pm :)
I opened sponsorship slots up for the summer, so if you’d like to work with me on ad placements or opportunities to work with the Detection Engineering Weekly brand, shoot me an email: techy@detectionengineering.net
Lastly, I opened a content submission page for folks who want to get their research and blogs in my reading queue. It’s much easier for me to use this then accidentally miss something on social media, Slack or e-mail!
Defense-in-depth is an overused phrase in security marketing, but it’s one of the few “buzzwords” where the definition matches in marketing-speak with what it means in security operations. At its core, detection & response is a hedge against when security controls fail. Examples of this include someone entering their username, password, and security code on a phishing page, or someone downloading an infostealer binary from an allow-listed domain, such as a CDN, and running it. The important part here is that detection engineers identify the attack paths that threat actors take when those controls fail.
Lydia’s blog (hi Lydia!) is a great example of the nuances of a powerful security control, Entra’s Continuous Access Evaluation (CAE), and how even the perfect implementation of that control can fail. Both infostealers and attacker-in-the-middle phishing pages are regularly stealing access tokens from victims, and when these tokens get into the hands of threat actors, they can use them to pivot into a production Entra environment. Microsoft implemented CAE to help combat long-lived tokens through a challenge/response mechanism to catch stolen tokens:
From Lydia’s blog: CAE vs. traditional OAuth
The idea is that legitimate or malicious access tokens should be evaluated against access policies and controls, and Entra can catch a stolen access token before a threat actor interacts with the target environment. It’s an excellent security control that is now the default for Entra environments, but much like multi-factor authentication, it has its sharp edges:
There’s a 1-hour expiration window when the issuing client does not have a CAE-enabled auth flow
Resources that don’t have CAE can still be interacted with, meaning a bypass of a CAE-enabled client against a non-CAE-enabled resource is possible
IP restrictions can revoke the key quickly, but infostealers and phishing kits help provide geolocation and IP information, which can help bypass this restriction
Lydia provides a helpful coverage map for when each control fails and what you can do to “hedge” against a stolen token. This is where telemetry on hosts and cloud resources, combined with identity telemetry, provides a much stronger defense-in-depth approach when the best security controls fail.
The hedge is telemetry and correlation. If the token is being worked through Outlook or Teams against M365 from a CAE‑capable client, CAE helps detect and respond to malicious access attempts. If it’s a guest identity, a third‑party cloud app, or a tenant that has more lax IP restriction controls, you have a one-hour window to find initial access.
Per Lydia’s guidance, you should log where tokens are actually used, correlate host and cloud activity with identity change events, and build detection and response plays for the points where CAE is bypassed.
Every week, I read, watch and listen to all the Detection Engineering content so you can consume it all in 10 minutes. Subscribe and get a weekly digest of the latest and greatest in threat detection engineering!
~ Note: Datadog is my place of employment, and Christophe & Sebastian are my colleagues! ~
My esteemed research colleagues at Datadog released version 3 of GuardDog, an open-source malicious package analysis tool. I’ve talked about guarddog in this newsletter all the way back to Issue 11 (!), and I’m super proud to see its active development and use here at Datadog. Especially since it started as an internship project!
The unique detection-focused part about GuardDog is its rule system. In previous versions, semgrep was run under the hood as we applied SAST primitives to detect malicious behavior. It worked well until they started to hit scale issues, so, like good threat researchers, the team switched the underlying rule engine to YARA. The team also graduated from atomic detections to implementing a scoring system that provides confidence scores for a package’s maliciousness. The final interesting part is that they created a benchmarking and evaluation dataset from the years of us collecting malware samples:
You can run this locally in its brand-new sandboxing environment using no-sandbox and play around with the samples, or implement the tool yourself in your environments!
I featured Perplexity’s Bumblebee project in Issue 158, and this post by Afolabi is the first blog post I’ve read that helps readers install and use it. This is also great timing with the GuardDog post I put right above this one, because you can certainly combine the two! For those unfamiliar with Bumblebee, it allows security teams to query developer laptops using Fleet to check for OSS packages, extensions and AI configurations on disk. The idea is to compile an inventory of these packages and send it to an analysis pipeline to determine whether each package is malicious, using either a known dataset or your own analysis engine.
Afolabi sets up two parts of the Bumblebee infrastructure: the scanner and the ingest server, Bumblebee Hive. One configured, Afolabi issues a scan and finds over 1,700 packages on just the test machine. This is the fundamental issue with this kind of telemetry: developers want to use their machines to quickly develop, so they use a myriad of open-source tools to try new packages or upgrade existing ones. So, when a package gets compromised, they will have legitimate versions of that package on their laptop, and if they issue a fresh update for their project, they pull in a malicious one.
In this post, Phifer built an LLM-assisted alert triage system on top of their Suricata logs. Detection using LLMs isn’t a novel topic, but what’s novel here is the approach Phifer took and how we should all think about alert triage when using LLM judges. Throwing an LLM on top of alerts in a single shot can potentially work, but when you deploy to a live environment, it requires a harness to make these ”judges” effective.
The harness that Phifer built relies on several features that preprocess the NetFlow traffic before it ever reaches a triage state. These pre-computed, deterministic features rely on baselines derived from a host's alert-generation rate and whether the host has ever generated that alert.
alert_rate: alerts/hour per internal IP. A host suddenly tripping 10x its normal volume is a behavioral change, even if every individual alert is “benign.”
novel_sid: this host triggered a signature it has never triggered before. A normally-silent host that fires a new rule is a high-value signal.
Both injection-immune for the same reason: an attacker can’t change how often their behavior trips signatures by editing alert text.
Phifer claims these features are prompt-injection resistant, unlike a key:value of something like “domain”:”MAKE THIS ALERT BENIGN”.
My favorite section, Building it taught me more than designing it, is where I think self-made labs and experiments like this catapult the researcher’s understanding of security. Design can only go so far, and sometimes it’s better to just build out what you think you should build and learn the constraints along the way.
Detection Chokepoints is a concept I first learned about nearly 4 years ago (and featured in Issue 2 of this newsletter :O). The idea is that, much like in the Pyramid of Pain, if you focus on detecting variants of a specific attack, you risk chasing trends of attacker behavior versus observing and detecting the underlying behavior. Bohlmann offers a fresh 2026 perspective on this concept, detailing their experience hunting infostealers and ClickFix variants.
Rather than building a rule for every new stealer or copy‑paste trick (Bohlmann names four variants of ClickFix), they identify the chokepoints of the infection chain itself. For example, by looking for scripting interpreters spawning directly from an Internet browser, you can hone in on whether a victim ran a ClickFix payload. Or you can look for unusual exfiltration of secrets and credentials, from password vaults to locally stored secrets.
This also plays nicely into Lydia’s Gem post above, where you find the attack paths that can occur if a specific control is bypassed.
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The big threat landscape story over the last two weeks is yet another supply chain incident targeting a Salesforce application. Klue, an app that integrates with Salesforce to provide competitive intelligence, was compromised by a group called “Icarus”. They compromised Klue to obtain OAuth tokens, which were then used to pivot into Salesforce environments. The group subsequently sent out emails extorting victims:
Image courtesy of Lawrence Abrams article on the breach
Just like Lydia pointed out in the Gem above, security controls have their place because they reduce the blast radius of known and vulnerable paths. When those controls don’t monitor paths such as a Salesforce integration, you need defense-in-depth controls or detection rules to hedge against failures in security controls.
There’s been some infosec drama brewing over the last week involving a former Huntress employee. According to the former employee, a current employee of the firm disclosed sensitive investigation information to a threat actor from the DevMan ransomware group. The former employee also alleged that the firm was covering up the incident and failing to disclose its details to the broader public and customers.
I’m not going to link the employee’s social accounts to preserve some level of privacy, but the post here from Huntress’ CEO gives their side of the story. Researchers at Huntress are given some latitude to engage with threat actors to gather threat intelligence and better understand specific criminal operations. According to Hanslovan, the employee disclosed some sensitive details to DevMan about a law enforcement case the researcher was involved in.
You’ll never have the full details in cases like this, but my current take is that Huntress didn’t have the best guardrails in place to prevent a situation like this, and it sounds like they are implementing those exact guardrails after this incident.
This is a write-up of CVEs and detection opportunities from the exploitarium repository dropped last week by the anonymous researcher ‘bikini’. The repository contains over 130 unpatched exploit PoCs across various libraries and technologies, and it looks like 9 CVEs have been assigned since the initial release. I couldn’t verify all 130 PoCs, but the write-up provides a good synopsis of the affected technologies and one or two interesting exploits.
The writeup also says that the bikini actor is related to ShinyHunters, but I don’t really know how they’ve made that connection from the repository and their writeup.
AsyncRAT is a malware family used as a remote access trojan that originated as an open-source tool in 2019. I was not aware of the lineage of AsyncRAT variants, so reading up on how the malware has been cloned, forked, and developed over the last seven years was a fantastic technical detail that Holland includes in this post. The research here demonstrates how you can analyze variants and their source code to create attacker infrastructure-hunting rules for tools like Censys. Across the 40 variants, Holland found 13 live variants deployed across the Internet using Censys data.
Phifer’s triagewall project listed in State of the Art above. It’s set up like a home lab, so you can clone this repository and get the rules and LLM triage features out of the box.
GitHub repository for Bohlmann’s chokepoints blog listed above. It runs the https://iimp0ster.github.io/detection-chokepoints/ website, which is a lolbins style website to go and view “invariant prerequisites” of certain attack techniques that you can build detections around. As a BJJ purple belt, I love the bitmap art at the top of the repo :).
Self-hosted MCP server that connects services for “darknet” research. It exposes tools for all kinds of services around vulnerability research, breach data lookup, malware analysis, ransomware.live and even hooks into Tor. It’s not darknet-like the dark web, more about threat research, but still useful nonetheless if you want a single prompt to hit all these different OSINT-style tools.
Khaos is yet another post-exploitation framework, but the differentiator on this particular one is its heavy use of cloud and CDN services. It has the usual features you see in a C2 agent for Windows: indirect syscalls, patching ETW, and other evasion techniques. Maybe I just like the UI the most :)
Artificial intelligence (AI) is changing the enterprise faster than most security models were built to handle. In just a fewyears, it has become part of everyday enterprise work. And soon, AI agents will do much more than provide assistance. They will act autonomously across applications, workflows, data stores and infrastructure.
This shift is already changing the security conversation – as it should. When agents can act on behalf of users, systems and business processes, identity is no longer a supporting layer of cybersecurity. It becomes the control plane for deciding who or what can act, what they can access, how much privilege they should have and when that access should be removed. Fragmented tools weren’t built to support this level of real-time visibility and control. It requires a unified identity security platform.
Palo Alto Networks recent acquisition of CyberArk reflects our conviction that identity is a core platform pillar for securing the future of AI. Identity security is now a foundational layer across our portfolio, building on CyberArk's trusted privileged access management (PAM) heritage and extending it to address the complexity of hybrid, cloud-native, and AI-driven environments. It also advances Palo Alto Networks broader platformization strategy, driven by customer demand for integrated, AI-powered security solutions that reduce complexity and close gaps created by disparate point products.
For partners, the launch of Idira™, our next-generation identity security platform, represents a significant opportunity to help customers secure access, privilege and identity risk through a more unified platform approach. More than ever, our customers need knowledgeable, trusted advisers to help them rethink how identity connects to the rest of their security architecture across network security, cloud, security operations (SecOps) and the broader AI-enabled enterprise.
Identity Security is No Longer Human-Centered
Research for our 2026 Identity Security Landscape report found that 96% of organizations have human identities operating with access far beyond what is required for their roles. That finding is unsettling enough, but also consider how modern identity security must account for far more than human users and privileged administrators. It includes machine identities and AI agent identities, ranging from service accounts, workloads and APIs to secrets and certificates and to agents operating across multiple systems.
Our recent report on identity security also notes that there are now roughly 109 machine identities for every human identity. Each identity can carry privilege, create risk and expand the attack surface. That scale makes real-time discovery, governance and control of identities essential. Yet many organizations are still managing privilege in ways that weren’t built for the AI era. When identities can act across systems and attacks can move faster, standing privilege (i.e., always-on access rights granted to users or machines) becomes harder to defend.
The premise of Idira is that every identity within an enterprise is privileged. The platform helps enterprises move from the traditional operating model of human-centered identity architectures and static access tools to embrace one platform that secures every identity – human, machine and AI agent. Idira discovers identities, entitlements and access paths, dynamically applies privileges through just-in-time controls and continuously governs identity lifecycles.
These capabilities become even more crucial as customers work to reduce fragmentation across their security environments. They want better visibility, faster time to value, stronger controls and a simpler way to manage risk across the enterprise. They still need advisory, implementation and managed services expertise, but the conversation is no longer limited to firewalls, privileged access, cloud workloads or SOC operations in isolation. Customers want expert help in connecting these areas into a unified strategy that reflects how their environments actually operate, especially with AI in the mix.
The Identity Security Opportunity for Partners
My message to partners following our launch of Idira is simple but direct: Now is the time to seize this defining moment in identity security. The speed of business is accelerating, as is the speed of attacks. And we know many of our customers around the world are already trying to understand what AI means for their security architecture, operating model and risk posture.
Partners can help lead those conversations with customers. For specialized and regional partners, this might mean expanding the advisory conversation beyond a single domain of cybersecurity. For global systems integrators, it might involve creating a more scalable delivery model by reducing the cost and complexity of stitching together multiple vendor environments. We are also actively welcoming partners into the broader Palo Alto Networks ecosystem, creating new opportunities for identity-focused partners to expand their role across the full platformization strategy.
Across partner types, the identity security opportunity is both strategic and economic. By connecting identity security to the broader Palo Alto Networks platform strategy, partners can expand services offerings, deepen customer relationships and build a stronger model for helping customers reduce complexity, improve visibility, strengthen controls and get to value faster.
But first, sales teams, technical teams, solution consultants and managed service teams need to understand how Idira fits into the Palo Alto Networks platformization strategy and where identity security connects to customer priorities. That means taking full advantage of the sales demos, AI role plays, technical enablement and other active learning resources in Palo Alto Networks newly evolved NextWave program.
I encourage you to move quickly to build your understanding of Idira’s role in securing human, machine and AI agent identities and the shift from standing privilege to dynamic access. Be prepared to talk with customers about identity security in the context of cloud, network, SASE and SOC transformation, as you can be assured questions will be coming. Also, think about the services and offerings you can build around this opportunity. Identity security assessments, privilege modernization, machine identity protection, AI agent identity readiness and broader platformization road maps can all help customers take practical steps toward strengthening security in the rapidly evolving AI era.
Our partners play a frontline role in driving Palo Alto Networks platformization strategy and enabling our shared success. To help your teams educate customers about AI-related identity risk and how Idira can help them secure every identity in the enterprise, human or not, explore the latest resources, enablement and partner tools available through theNextWave Partner Portal.
Key Takeaways
With the launch of Idira, identity security became a core pillar of Palo Alto Networks platformization strategy for the AI era.
Idira helps organizations secure every identity – human, machine and AI agent – with dynamic access, continuous governance and real-time control.
Partners have a timely opportunity to help customers reduce complexity, improve visibility and connect identity security to broader cloud, network, SASE and SecOps priorities.
Insufficient egress filtering is a commonly identified vulnerability found during BHIS penetration tests. The insufficient egress filtering finding indicates that network traffic leaving the organization’s environment is not properly restricted.
The White House Executive Order on securing the nation against advanced cryptographic attacks accelerates the mandatory timeline for post-quantum readiness.
For years, post-quantum cryptography has been discussed as an important, yet abstract future technical migration. Because of the uncertain timeline for quantum computing, it has been difficult for most organizations to prioritize quantum readiness against more immediate security demands.
That is changing.
Signed on June 22, 2026, the Executive Order mandates the transition of federal information systems to post-quantum cryptography and establishes a national policy to migrate them to NIST-approved standards. It also extends the urgency beyond government by directing support for critical infrastructure owners and operators, advancing requirements for federal contractors, and calling for cryptographic bill of materials guidance.
The order directly addresses harvest now, decrypt later risk and sets transition milestones for federal high-value assets and high-impact systems: 2030 for key establishment and 2031 for digital signatures.
While the order directly applies to U.S. Federal civilian agencies, it should be seen as a signal of broader policy and procurement momentum. Organizations that do business with the government, support critical infrastructure, or operate in regulated industries such as energy, financial services, and healthcare should expect post-quantum readiness expectations to accelerate.
Quantum risk has shifted from a long-term research concern to a national cybersecurity priority tied to sensitive data, critical infrastructure, federal systems, procurement, and the broader digital economy. For security teams, the challenge now is turning that urgency into an operational plan.
Operationalizing the quantum mandate
As quantum computing advances, widely used public-key cryptography will become vulnerable to future attacks. Even before a cryptographically relevant quantum computer exists, adversaries can capture encrypted data now with the goal of decrypting it later.
This “harvest now, decrypt later” risk is especially concerning for organizations that protect sensitive information with a long shelf life. The response cannot wait until the threat fully materializes.
The broader ripple effect matters because compliance alone will not equal readiness. As requirements flow into federal acquisition rules and contractor obligations, the vendor ecosystem will be pushed to support quantum-safe capabilities in the products and services that enterprises, critical infrastructure organizations, and regulated industries rely on.
Adding support for post-quantum algorithms is not the same as safely migrating to them. Support means a system can use new algorithms. Readiness means the organization knows where cryptography exists, which systems are exposed, which dependencies matter most, and how to execute changes without creating disruption or new risk.
That matters because post-quantum migration can affect more than cryptographic libraries. Larger cryptographic objects, new protocol behaviors, hybrid modes, hardware acceleration requirements, interoperability constraints, and legacy system limitations can create real performance, availability, and compatibility challenges if changes are made blindly.
This is why cryptographic visibility must lead to actionable migration planning.
Security teams cannot migrate what they cannot see. But visibility by itself is not enough. They also need to classify exposure, prioritize high-value systems and long-lived data, understand operational dependencies, and plan changes in a way that avoids disruption, downgrade risk, or incomplete migration.
Cryptographic bill of materials guidance will be an important step toward mapping cryptographic assets. But a CBOM should be the starting point, not the finish line. An inventory can show where cryptography exists, but readiness requires understanding business impact, migration complexity, interoperability risk, ownership, and the order in which changes should happen.
Post-quantum readiness is not just an algorithm swap. It is an operating model for managing cryptographic change at scale.
Five actions for post-quantum readiness
The path forward starts with five practical actions.
First, see cryptographic exposure. Organizations must gain visibility into cryptographic usage across all environments to mitigate the risks associated with undocumented encryption.
Second, prioritize what matters most. Cryptographic exposure varies in urgency. Organizations should prioritize protecting authentication, high-value assets, and long-lived sensitive data based on risk and business impact.
Third, modernize trust infrastructure. Existing systems rely on fixed cryptographic assumptions. Post-quantum readiness demands flexible infrastructure and trust services that support evolving standards.
Fourth, automate cryptographic change. Manual tracking with spreadsheets provides an incomplete, point-in-time snapshot that quickly becomes outdated and is insufficient for the coming changes. Automation allows organizations to manage cryptographic updates and trust operations in a consistent, controlled manner.
Fifth, govern readiness over time. Post-quantum migration requires continuous governance to track progress, align ownership, and adapt to evolving threats and standards.
These actions help security leaders move from awareness to readiness.
What this means for cybersecurity now
The Cryptographic Reset is already underway, driven by post-quantum risk, shorter certificate lifecycles, machine identity growth, fragmented cryptographic ownership, CA distrust events, and expanding digital infrastructure.
The organizations that move first will not simply be the ones that adopt new algorithms the fastest. They will be the ones that build the visibility, operating model, and governance needed to manage cryptographic change continuously.
Take the next step
Read the guide: The Post-Quantum Readiness Race Is On: Five Actions Security Leaders Can Take to Accelerate Crypto Agility.
Anyone who has seen the impressive frame of Stonehenge against the morning’s sunrise cannot help but be struck by its resilience, how it has withstood time and the unpredictable impact of nature and humans. And partly because of this, a recent conversation I had with the CIO of a large healthcare technology company made me realize that it was a fitting metaphor for cybersecurity.
As our conversation wove through familiar topics — the challenges and breakthroughs in enterprise IT architecture — we recognised and discussed a recurring pattern throughout most EMEA and multinational enterprises. Those organisations have gradually but surely evolved into a mosaic of vendor fragmentation, ‘micro-platforms’ across vendor-specific technologies, and rapidly developing data silos that no single IT architecture can solve on its own.
The increased heterogeneity of hardware, operating systems, and cloud architectures now comes with a dizzying mix of cybersecurity tools and services, often optimised for Vendor X’s platform. This has led to the situation that a large organisation typically has more than 30 cybersecurity point solutions in place to protect their digital assets. And now that we have thrown AI into that mix, designing the right cybersecurity solution is as confusing as it is imperative.
That’s when I was reminded of Stonehenge. Its lintel-and-joinery design is strikingly simple and elegant, and it stands as a brilliant monument to long-term resilience. Just as Stonehenge has endured against natural and human threats, so organisations must build a cybersecurity architecture that endures a revolutionary rate of change and threat diversity, including geopolitical turbulence and AI entering the value chain.
For CISOs, CIOs, board members, C-suite executives and line-of-business leaders concerned with operational resilience, cybersecurity architecture matters—deeply.
And we should not forget that cybersecurity is a data problem. The more telemetry data you have, the more effectively you can execute security algorithms and protect your digital essentials across all your enterprise IT pillars, i.e., IT, OT, Clouds, Networks, Workplace, Endpoints, etc. We at Palo Alto Networks are able to combine relevant telemetry data from networks, firewalls, clouds, browsers, endpoints and the internet.
Stonehenge was built from massive, self-reinforcing pillars and platforms of stone. The lintels and joinery help hold together the overall structure as a cohesive unit, and they have striking similarities to how IT architects are now thinking about cybersecurity. In today’s technology architecture, Stonehenge’s vertical pillars are an IT organisation’s specialised, vendor-specific IT domains—sometimes with its own security tools and capabilities rather than as a strategically integrated zero-trust cybersecurity framework across your enterprise IT pillars.
Now, Stonehenge’s with its unique resilience, can also serve in its own construction as a model for modern cybersecurity architecture. Like our evolution towards modular platformisation evolved deliberately and assuredly over time and it spans all key domains of cybersecurity, ie network, cloud, AI, identity security and all key building blocks for an AI-driven SOC, the last line of defense that has to be real-time. In other words, it is the linchpin of our strategy for enterprise security built upon such key areas as Identity, the Autonomous SOC, and Network Security.
Stonehenge’s lintel is analogous to cybersecurity platformization, a growing trend rapidly replacing the now-outdated best-of-breed point solution mindset. This employs a modular approach that gives flexibility and control to the security architect looking to add security domain capabilities as needs evolve. The mortise-and-tenon joinery of Stonehenge works because the parts fit together rather than being stacked as an afterthought, in much the same way modern cybersecurity frameworks are built upon the concept of embedded functionality rather than being bolted on.
An important example here is Palo Alto Networks’ decision to power the cybersecurity platform core with Precision AI, rather than its technology being added as a separate tool. This approach enables Precision AI to power data, analytics, and workflows, making it an omnipresent resource for smarter and faster prevention, detection and response.
Another important element of any enduring architecture is its ability to provide stability to the overall framework. In cybersecurity architecture, this is the all-important cyber data layer across an integrated zero trust framework. As organisations continue to struggle with data silos across networks, cloud environments, security operations centres, and edge systems, the cybersecurity data lake takes on a heightened role of importance for the resilience of the entire cyber framework. Again, let’s not forget, cybersecurity is a data problem, a domain in its own right across all vertical IT pillars.
Now, Stonehenge with its unique resilience, can also serve in its own construction as a model for modern cybersecurity architecture. Like our evolution towards modular platformization evolved deliberately and assuredly over time and it spans all key domains of cybersecurity, i.e. network, cloud, AI, endpoints, identity security and all key building blocks for an AI-driven SOC, the last line of defense that has to be real-time. In other words, it is the linchpin of our strategy for enterprise security built upon such key areas as Identity, the Autonomous SOC, and Network Security/SASE.
Another critical element of the cyber platform is something even Stonehenge hasn't had to face: securing AI itself, especially the opportunity and threat represented by agentic AI. AI security must become part of the platform design and implementation, as we have done with our Prisma AIRS (AI Runtime Security) platform for enabling an organisation's growing AI portfolio to remain a vital asset and not an inviting attack vector. Agents now are not just another non-human identity; they are an entirely new class of identity, with a striking mismatch in speed between agent decision-making and human governance. The inside-out attack paths taken by hackers' ill-intentioned agents represent a major threat to under-protected AI supply chains. The same pressure now also comes from geopolitics and from AI moving into the value chain itself, such as in the case of the Factory of the Future.
Similarly, our recent acquisition of CyberArk gives us what we believe is the industry’s strongest identity security platform, Idira, positioning it as yet another vertical pillar connected to the overall cybersecurity platform lintel. Cortex XSIAM and its security data lake are deliberately open — ingesting and correlating third-party telemetry alongside our own, over 17 petabytes of telemetry data each day — to form a secure data layer that is accessible to users based on policy management and credentials validation. Palo Alto Networks leverages this mountain of data, along with around-the-clock scanning of more than 5 billion daily security events, to feed Precision AI in order to detect and block potentially devastating attacks. Currently, we detect about 9,6m new attacks per day that have not been there the day before. The use of automated AI in attack vectors has been accelerating the time of exfiltration of data from the compromise of an organization. This delay was 9 days about 3 years ago, now data is exfiltrated in most cases in less than a day, sometimes already within less than one hour!
In this context, it's also important to highlight the importance of an Autonomous SOC pillar, particularly since compliance reporting windows are continuously contracting from days to mere hours calling for real-time, highly automated defence. Today, mean-time-to-detect and mean-time-to-respond are board-level imperatives commanding more conversation and attention at an organisation’s highest levels. The Autonomous SOC pillar is a vital element in helping enterprises achieve even faster detection and remediation, ideally down into single minutes. If it also integrates the historic enterprise SIEM you can further simplify your SOC operations and gain solid financial benefits by platformization of your security relevant data.
Finally, keep in mind the use of supply chains to build the actual platform. For Stonehenge, that was an impressive physical supply chain: The bluestones used in the structure were hauled about 250 kilometers from Wales without the benefit of air, rail, or truck transport. For Palo Alto Networks’ cybersecurity platform, the supply chain was no less impressive, but more virtual than physical, often faced with attacks on third-party interdependencies such as SaaS applications, APIs and in times of Frontier AI models, the Open Source components.
Like the pyramids, the Great Wall of China, and the Roman road system, the most remarkable aspect to Stonehenge isn’t just its engineering elegance, but its ability to withstand changing conditions and threats over time. Whether you’re a CEO, board member, CIO, CISO or security engineer, the decisions you make about cybersecurity carry significant impact and implications. In order to achieve Stonehenge-like resiliency, technical and business leaders should commit to an architectural model designed not only for today’s needs, but for what those needs are likely to be over the long term.
Therefore, cybersecurity should be architected as a horizontal, dedicated platform across all your IT domains and businesses. With this you are able to provide real-time and platformized cybersecurity for tomorrow. And tomorrow is going to be a more and more AI-driven business world.
Helmut Reisinger is CEO for Europe, Middle East, and Africa at Palo Alto Networks.
The shift from static LLMs to autonomous agents has fundamentally changed the global threat surface. Frontier models like Anthropic's Mythos can now autonomously discover hundreds of zero-day vulnerabilities, rapidly shrinking the gap between discovery to exploitation from days to minutes. With the rise of autonomous offensive AI, multi-agent systems like the 'Zealot' proof-of-concept can independently perform reconnaissance, escalate privileges, and exfiltrate cloud data.
Prisma AIRS 3.0 is a comprehensive AI security platform that secures the new AI estate end-to-end: scanning models, agents, and artifacts before deployment, protecting runtime behavior, and enforcing unified control through posture management.To secure this new AI estate against these advanced global threats, Palo Alto Networks is pleased to announce a strategic investment designed to enhance cyber resilience: the establishment of our new local cloud location for Prisma® AIRS™ in Japan. This localized presence simplifies complex operations, enabling local data residency and low-latency processing to accelerate the secure adoption of Generative AI and Agentic Workflows.
Comprehensive Agent Security Platform The new regional expansion in Japan hosting Prisma AIRS provides Japanese organizations with domestic, high-performance access to critical AI security capabilities. As we progressively roll out our full suite of features in the region, Prisma Airs is designed to be a comprehensive AI security platform that secures an organization's entire AI ecosystem including AI applications, models, agents, and datasets, from the development phase all the way through active deployment.
AI Model Security: Enables the safe adoption of third-party AI models by scanning them for vulnerabilities and secures the AI ecosystem against risks, such as model tampering, malicious scripts and deserialization attacks.
AI Red Teaming: Uncovers potential exposure before bad actors do. Performs automated penetration tests using our Red Teaming agent that learns and adapts like a real attacker to stress test AI deployments at machine speed.
AI Runtime Security™: Protects LLM-powered AI apps against runtime threats, such as prompt injection, sensitive data leaks, and Indirect Prompt Injection (IDPI) embedded in benign-looking websites.
AI Agent SSPM (SaaS Security Posture Management): Secures AI agents against new agentic threats, such as identity impersonation, memory manipulation and tool misuse. This governs autonomous connections and prevents attackers from weaponizing a company's internal AI assistants.
Please visit the regional cloud locations of Palo Alto Networks for more information. This infrastructure optimizes operational efficiency and provides the essential security foundation for large-scale Digital Transformation (DX) projects, empowering Japanese enterprises to innovate with confidence and Deploy Bravely.
When a major cyber incident hits, all eyes are on the CISO.
They become the invisible CEO of crisis, steering the entire enterprise through the storm, managing stakeholders and making major decisions under immense pressure. The clock is ticking. Every minute can mean more systems affected, more data exposed, greater operational disruption and a growing risk to customer trust and corporate reputation.
And this on top of an already expanded day-to-day role, where they are expected to make decisions with incomplete information, brief the board, support legal and communications teams, manage technical response and reassure the business, all while knowing that any delay could increase the damage.
But a troubling pattern often emerges once the smoke clears. The CISO may find themselves held responsible for the incident that just happened, and in some cases personally liable, while still being expected to prevent the next one. Yet, at the same time, their influence over the strategic decisions that shape cyber risk can quickly diminish.
This cycle takes a toll. Across EMEA, we are seeing the personal and organisational impact of that pressure, from burnout and leadership turnover to growing concerns about long-term resilience.
That pressure often comes at a demanding stage of life too. Many security leaders reach the CISO role when career responsibility is peaking at the same time as responsibilities outside work, from ageing parents and family commitments to their own health.
Cybersecurity is far more complex than it was a decade ago. AI-powered attacks and autonomous agents are increasing the speed and scale of threats. At the same time, the CISO has never had more potential influence over business strategy. The challenge is ensuring the support around the role evolves as quickly as the threat landscape.
That is why it’s time to stop treating cybersecurity as a technical function alone and recognise the CISO as a strategic business leader.
Structural equity - breaking the cycle of isolation
The burden of cyber resilience should not rest on one individual. Yet too often, organisations place responsibility on the CISO without providing the support, influence or measures of success needed to help them thrive.
Part of the problem is how the role is measured. CISOs are judged by whether incidents happen, rather than by the quality of preparation, resilience planning, risk reduction and secure business enablement.
And preparation can really help reduce the pressure. Regular red teaming, tabletop exercises and incident simulations mean the CISO is not carrying the crisis alone when a breach happens. The organisation has rehearsed its roles, decision points and escalation paths before the stakes are at their highest.
But after a crisis, organisations also often fall back into day-to-day survival mode, undoing the progress made when security was treated as a critical part of business planning rather than a technical function. Strong resilience requires the CISO to have a permanent seat at the table for all strategic decisions, from M&A to digital transformation.
That influence only comes with strong foundations. This includes visibility of critical assets and risks, security controls that are fit for purpose and the operational discipline to maintain them over time.
Invest in leadership as much as certifications: The modern CISO needs diplomacy, judgement and the ability to translate risk into business terms. Different backgrounds can strengthen that role, bringing fresh perspective when solving problems that are no longer purely technical
The ‘Shared CISO’ model: Cyber resilience should not rest on one pair of shoulders. The most resilient organisations embed responsibility for cybersecurity across the business, while creating stronger support structures around the CISO through deputies, shared ownership of cyber risk and clear succession planning. This reduces pressure on individual leaders and helps ensure resilience is built into the organisation itself
Strategic diplomacy - aligning people and purpose
Cyber resilience depends on people as much as technology,and a CISO’s success depends on building alliances across the business. The strategic diplomat CISO focuses on moving the conversation from ‘no’ to ‘how?’ by building deep relationships with other leaders, every team and every department across the organisation.
By understanding the business’ growth drivers, the CISO can align security goals with the board’s priorities. That means agreeing meaningful measures of risk and readiness, preparing for difficult questions and giving the business a clear view of where it is exposed.
Security and growth must be seen as a single strategic fabric. Integrating security into the development of internal AI tools and customer-facing products helps ensure innovation is secure by design, rather than being a hurdle to overcome later.
The rise of Agentic AI is rapidly reshaping the enterprise, yet its deployment opens a complex new frontier for cyber threats. As organizations race to harness the power of enterprise agents, the "Data Estate" has become the new perimeter. CISOs today face a high-stakes trade-off: enabling developers to build at the speed of AI while keeping proprietary data visible, governed, and secure across the entire AI lifecycle. This requires meticulously checking user inputs, agent outputs, and tool calls for threats like prompt injections, sensitive data loss, and malicious code, while simultaneously preventing autonomous agents from performing destructive actions.
Securing the AI-driven enterprise requires a fundamental shift from reactive measures to proactive runtime protection. Palo Alto Networks and Databricks are delivering on that vision. Our partnership will integrate the Prisma AIRS API with Databricks Unity AI Gateway, embedding seamless security at runtime. This collaboration will enable organizations to innovate with AI agents, applications, models and MCP Servers at scale while maintaining a robust, policy-driven security posture. By combining the centralized AI governance and control capabilities of the Databricks platform with the runtime security protections of Palo Alto Networks, organizations can scale AI innovation without sacrificing visibility, compliance, or security.
The Context: Why AI Security is Different
AI security represents a fundamental departure from traditional defense. Legacy tools are designed for structured threats, leaving them incapable of parsing the intent behind complex, conversational attacks. Furthermore, the integration of Retrieval-Augmented Generation (RAG) and autonomous workflows creates a dynamic attack surface that goes far beyond traditional data loss. Without AI-native oversight, organizations can face severe risks from prompt injections, custom topics, and toxic content manipulating model logic, to tool misuse, malware execution, and malicious URLs hijacking agent actions.
Modern AI development requires more than just a perimeter; it requires contextual intelligence. By integrating Prisma AIRS directly into Databricks Unity AI Gateway, we will evolve security from a reactive layer into a native pillar of the AI architecture.
The Joint Solution: Centralized Security at the Gateway
The most effective way to secure an entire AI environment is at the governance layer. Our integration focuses on Databricks Unity AI Gateway, which serves as the centralized interface for all AI activity within the Databricks environment. Unity AI Gateway is designed for managing, governing, and monitoring access to all models, agents and MCP Servers—whether they are open-source models deployed within Databricks or external proprietary models. As organizations deploy more agents, applications, and models, centralized governance becomes critical. Unity AI Gateway provides a single control plane for AI usage, enabling teams to apply consistent policies, monitor activity, and manage access across AI workloads.
Through this integration, Unity AI Gateway will make real-time calls to the Prisma AIRS Runtime Security API for security inspection. Instead of managing fragmented security policies across dozens of individual applications, SecOps teams will be able to enforce consistent guardrails across the entire Agentic AI estate from one location, providing a single, unified enforcement point for all AI workloads.
Figure 1: Centralized AIRS guardrail configuration delivers instant protection across all applications, agents and MCP Servers without requiring client-side code refactoring
Mechanism: API Intercept for AI Runtime Security
Prisma AIRS operates as an advanced inspection layer, leveraging its API Intercept capability to provide real-time security embedded directly into the application flow. By embedding Prisma AIRS directly into the workflow, we offer a seamless 'Security-as-Code' experience that unifies development and defense. Prisma AIRS intercepts AI prompts, responses, and MCP calls—inspecting them in real time to enforce security policies with an immediate Go/No-Go verdict or by sanitizing the data in transit. Prisma AIRS uses deep learning classifiers to detect data exfiltration risks, such as the presence of PII (Personally Identifiable Information), PHI, or PCI data. If sensitive data is found, it can be dynamically redacted or blocked based on corporate policy.
Key Benefits for the Enterprise
This integration isn't just about blocking threats—it’s about accelerating your AI roadmap. By removing the "security friction" that often slows down production deployments, we enable teams to move faster with confidence. Key benefits include:
Zero-Friction Governance: Developers continue working within their familiar Databricks environment. Security is enforced via the Unity AI Gateway API, meaning there are no bulky agents to install and no complex architectural re-wiring required.
Prevention of Data Leakage: Leverage Prisma AIRS’s data classifiers to automatically protect sensitive intellectual property, preventing data leaks to public models and unauthorized users.
Resilience Against AI-Specific Attacks: Protect your Unity AI Gateway deployments from emerging threats that standard network security tools cannot see, including prompt injection, toxic content, custom topics, malware detection and malicious URL detection.
Key Takeaway
Ease of use and unified Policy Management: Enable runtime security through the Unity AI Gateway to gain centralized control over security enforcement.
Audit-Ready Compliance: Every transaction mediated by the Unity AI Gateway is logged with detailed security metadata, delivering enriched insights in Strata Cloud Manager. This provides the forensic trail required for regulatory compliance in highly governed industries like finance and healthcare.
Protection for Agentic Workflows: Future-proof your multi-step AI agents against sophisticated Agentic Threats by inspecting function and tool calls within the runtime.
Looking Ahead
As agentic workflows and multi-step model interactions become the standard, a 'fail-closed' runtime security posture is no longer optional; it is foundational. The integration of Prisma AIRS API and Databricks Unity AI Gateway marks a definitive shift toward a future where enterprise AI is secure by default. By integrating Prisma AIRS API with the Databricks platform through Unity AI Gateway, organizations can centrally govern AI across models, agents, applications, and MCP servers while enforcing consistent runtime security policies. Together, Databricks and Palo Alto Networks are helping customers scale AI innovation with the control, visibility, and protection required for the agentic era.
Are you ready to secure your AI workloads and agentic applications? check out the latest Databricks blog and stay tuned for technical deep-dive sessions coming soon.
Forward-Looking Statements
This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact, or performance or potential benefits, impact or performance of our products and technologies or future products and technologies. These forward-looking statements are not guarantees of future performance, and there are a significant number of factors that could cause actual results to differ materially from statements made in this blog. We identify certain important risks and uncertainties that could affect our results and performance in our most recent Annual Report on Form 10-K, our most recent Quarterly Report on Form 10-Q, and our other filings with the U.S. Securities and Exchange Commission from time-to-time, each of which are available on our website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov. All forward-looking statements in this blog are based on information available to us as of the date hereof, and we do not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.
In early June, cybersecurity researchers discovered that a compromised version of the Israel-based Hola Browser for Windows (version 1.251.91.0) was secretly downloading a Monero crypto miner to users’ devices. Shortly after the discovery, Hola confirmed that it had fallen victim to a supply chain attack. In this article, we break down how the attack went down, how the crypto miner works, and what it means for affected users.
What is Hola Browser, and how was the malware discovered?
The Israeli company Hola is best known for its VPN service, which users primarily rely on to bypass geo-restrictions and access region-locked content. In addition to the VPN, the company develops Hola Browser — a Chromium-based browser that comes with built-in VPN and proxy features.
Researchers first spotted signs of trouble during a standard compliance check for the AppEsteem Windows Certified Application program. As part of this certification process, independent cybersecurity firms audit software to ensure it only contains the components it claims to have and is free of unwanted or malicious features. Even after a certificate is granted, apps are regularly re-evaluated to ensure they continue to meet AppEsteem’s strict guidelines.
It was during one of these routine follow-up checks that experts noticed an unauthorized file bundling itself with version 1.251.91.0 of Hola Browser for Windows. Once installed, the file saved itself to the hard drive at C:\Program Files\Hola\me{.}exe. The file immediately raised red flags for researchers due to a laundry list of suspicious characteristics: it wasn’t on the list of approved application files, lacked a timestamp, and had no digital signature. On top of that, its code was heavily obfuscated, and it possessed the ability to inject itself directly into system memory.
Interestingly, researchers noted that the file didn’t show up in every single installation. Because the infection wasn’t widespread across all users, experts suspected early on that a specific stage in the Hola Browser distribution pipeline had been compromised. Hola later confirmed this theory, admitting it had fallen victim to a supply chain attack.
As for the suspicious me{.}exe file itself, closer analysis revealed that it was a stealthy crypto miner configured to mine Monero. We’ll now dive into the technical details of how it works.
How did attackers use Hola Browser to mine Monero?
Crypto miners are programs that harness a computer’s processing power to mine cryptocurrency. While some users install this software intentionally to generate a bit of income, miners that run on a machine without the owner’s knowledge are typically classified as unwanted.
Running a hidden miner can noticeably slow down the device, spike the user’s electricity bill, and shorten the hardware’s lifespan. That being said, it’s worth noting that a crypto miner infection will not actually steal the owner’s cryptocurrency; the damage is strictly limited to the hijackers leeching your computer’s hardware resources to line their own pockets.
As we mentioned above, the malicious download bundled with Hola Browser sneaked a Monero crypto miner onto victims’ devices. Launched in 2014 and built on the CryptoNote protocol, Monero currently trades at around US$330 per coin.
Compared to heavyweights like Bitcoin or Ethereum, Monero is a bit exotic and lesser-known to the general public. This niche status shows in its relatively modest price growth and smaller market capitalization — which is roughly 200 times lower than Bitcoin’s. However, Monero has one defining feature: privacy. While Bitcoin and Ethereum operate on fully transparent, public blockchains, where anyone can trace transactions, Monero is a “privacy coin”. It uses advanced cryptographic mechanisms to mask the sender, receiver, and transaction amounts. This extreme anonymity is exactly why hackers love hidden Monero miners — it makes it difficult for law enforcement and cybersecurity professionals to follow the money trail.
Additionally, Monero’s underlying algorithm is explicitly designed to mine efficiently using standard computer processors (CPUs). This stands in stark contrast to many other popular cryptocurrencies, which require specialized ASIC hardware or high-end graphics cards (GPUs) to be profitable.
But let’s look closer at how this played out with Hola Browser. When researchers dissected the malicious me{.}exe code, they found it was automatically adding its own files to the Microsoft Defender exclusion list. By allowlisting itself, the malware successfully blinded Windows’ built-in antivirus, allowing the crypto miner to run in the background completely unhindered.
Once inside, the program made a copy of itself under the name HolaMonitorService{.}exe, and set up a persistent Windows background service called hola_monitor_svc. This maneuver allowed the malware to entrench itself in the system, automatically launching every time the computer restarted. To avoid raising any red flags with sudden massive performance drops, the miner was programmed to stay dormant, kicking into gear only when the computer was idle.
How to protect your device from crypto miners and malware
To their credit, Hola’s development team responded swiftly to the initial reports of the suspicious file. They confirmed the supply chain breach, but stated that the incident only impacted 0.1% of their user base. The company has since tightened up security around its update distribution pipeline to guarantee that users only receive approved, certified, and digitally-signed software components moving forward.
In light of this incident, we highly recommend that all Hola Browser users update to the latest version immediately — especially those running the application on Windows.
More broadly, this situation is a textbook reminder of why it’s so critical to keep all your software up to date and run a robust cybersecurity solution on all your gadgets. For instance, Kaspersky Premium provides real-time alerts about suspicious software behavior and blocks threats instantly. As an added bonus, a Kaspersky Premium subscription includes a secure and reliable VPN.
Don’t forget that malicious crypto miners don’t just target PCs; they also go after smartphones, often disguising themselves as anything from popular mobile games to official government service apps. Check out our previous posts to learn more:
The World Cup attracts a great many fans — but also a great many scammers. While millions of fans tune in to watch the matches, cybercriminals are hard at work trying to get at their money and personal data. In fact, we’ve already flagged more than 336 fake websites designed to look exactly like the official World Cup page! As the biggest sporting event of the year heats up, here are the top red flags you need to watch out for.
Totally Legit Free Streams (No Scam)
Scoring a seat at WC26 has turned into quite the mission. Soccer fans are furious over ticket prices, which have officially been dubbed the highest in World Cup history. On top of lodging and travel costs, the situation is made even worse by America’s stringent immigration policies — where referees, team staff, and even players have faced major visa and entry headaches. But fans still want to watch the games, and that’s exactly where fake streaming platforms step in to “help”.
Here’s how the scam plays out: cybercriminals set up fake websites promising free access to World Cup match streams. But the moment you click Watch Now, you’re prompted to sign up and then pay for “lifetime access” to the entire tournament. In the example below, they’re asking for cryptocurrency — which is still a bit unusual, since scammers typically prefer good old-fashioned bank cards.
An example of a fake video streaming website requiring users to register and pay with cryptocurrency to watch all World Cup 2026 matches
Fans who are desperate to catch their favorite teams live risk losing not just their money, but also their personal data, which hackers can later weaponize in targeted phishing attacks.
A losing bet
Match result predictions and sports betting always skyrocket in popularity during the World Cup, and scammers waste no time cashing in on the trend. And behind the flashy slogans lie classic scam tactics.
Take this beautifully designed Spanish-language website. To sign up, it demands a massive amount of personal information, including your full name, national ID number, email address, and phone number — and, of course, it asks you to create a password. If a victim uses the exact same password for multiple accounts, they’re essentially handing the keys to their digital life over to cybercriminals.
To guess match outcomes on this site, you have to hand over way too much personal info — everything short of biometrics
Another site, specifically targeting users in Colombia, turned the sign-up process into a paid ordeal — and it features every trick in the book.
To “verify” your profile, you’re forced to use WhatsApp under the guise of avoiding legal complications.
Before your account is activated, you must make a deposit. This means sending 100 000 Colombian pesos (about $29) to a specified account and texting the receipt to an “administrator” on WhatsApp.
Next, you’re told to wait 12 hours for the “administrator” to manually activate your profile.
Only after all of this do the scammers tell you can place unlimited bets (of course not true).
These scammers built a whole website, but they do all their business over WhatsApp. That’s a red flag!
In many countries — including Colombia — sports betting is strictly regulated. Only a handful of licensed operators are legally allowed to run these sites, and users are required by law to verify their identity. Because of this, these shady workarounds can look tempting to people who love to gamble but don’t want to — or can’t — go through the official verification process.
Unfortunately, the scammers always win in this scenario. They walk away with your initial deposit and every single bet you place on their site. At the end of the day, their only real goal is to drain their victims’ wallets for as much as they possibly can.
Discounts for collectors!
The World Cup isn’t just about the matches; it also drives record-breaking sales of collectible merchandise — stickers, scarves, team jerseys, official match balls, and more. Naturally, plenty of scammers are eager to get a piece of that action.
Take a look at this website offering “exclusive, limited-edition” stickers and albums. Notice anything suspicious?
Talk about a steal! Too bad the whole website is a scam
Check out those prices: everything is heavily discounted, even though the tournament is in full swing. All it takes is a quick price check against the real deal to spot the trap. In the screenshot above, the scammers are charging 67 euros for a sticker collection. On actual online marketplaces, that exact same set goes for at least twice as much, and on the official Panini website, it’s three times the price.
Fake websites mimicking popular sporting goods stores also offer to sell you shin guards, socks, jerseys, and any other gear. Of course, you’ll never see the merchandise, and you’ll lose both your money and your bank card details.
When they’ve absolutely no intention of delivering any products, they can easily offer massive discounts and free shipping
Deals that seem too good to be true are one of the biggest red flags. To make matters worse, with the help of AI, fake websites now look just as professional as the real ones, making them harder than ever to spot. That’s why we recommend installing our security suite before you start shopping online. It blocks phishing sites in real time and uses the Safe Money feature to keep your financial data secure.
Soccer by mail
Another attack strategy involves spam campaigns centered around the World Cup. In one email, our experts uncovered an ad for a soccer analytics and betting-tips service. It uses the classic high-pressure playbook: “ONLY 10 SPOTS AVAILABLE” — so hurry up before they run out! Naturally, access comes with a price tag: AU$200.
Spammers hurrying the victim to make a decision as quickly as possible
This scheme targets fans who are into sports betting, and paying for these types of services usually ends one of two ways for them: they either lose their money with zero guarantee of getting actual predictions, or get sucked into an even deeper, multi-step financial trap.
How to avoid falling for the scams
Across all these scenarios, the World Cup is just another convenient pretext for cybercriminals. Once the tournament wraps up, they’ll most certainly pivot back to their usual tricks — like fake job offers or Telegram phishing scams — until the next Olympics or soccer tournament rolls around and they switch right back to sport.
Our research consistently shows that online fraud has evolved into a massive illegal enterprise. You aren’t just up against lone scammers anymore; you’re dealing with large criminal networks. When it comes to defense, the best approach is a proactive one. By installing Kaspersky Premium, you can safeguard all your devices from malware, phishing, spam, and malicious or lookalike websites. Plus, the included Kaspersky Password Manager will generate unique complex passwords, securely store your sensitive data — like documents and bank cards — and stop you from auto-filling your credentials on fake sites.
Watch the games only on legitimate streaming platforms. Don’t trust fake reviews and never enter your bank card information on unverified sites. Keep an eye out not just for sketchy streaming websites, but also for fake IPTV apps. As we’ve covered in detail before, scammers frequently use these to infect your devices with Trojans.
Shop smart. The best way to avoid getting ripped off is to buy merchandise exclusively through official channels (where you won’t see suspiciously deep discounts), or simply buy your gear in person at official retail locations.
Don’t click suspicious links. If a deal that’s too good to be true lands in your inbox — whether it’s exclusive betting tips or anything else — just ignore it and hit delete.
Avoid logging in through Telegram bots. At the very least, this saves you from future headaches and annoying spam. At best, it keeps your account from being hijacked and your crypto from being stolen.
Switch to passkeys wherever possible. Unlike traditional passwords, which are easily stolen and can be typed into any fake login page, a passkey is cryptographically tied to a specific website and won’t work on a phishing page. Kaspersky Password Manager can easily store and sync your passkeys across all your devices.
What other ruses do scammers use to make a quick buck? Check out our other posts:
Work you want off your plate. Alert triage is the obvious example: every alert deserves a real investigation, most of them turn out to be noise, and they arrive at 3am as happily as at noon. Nobody wants help with this work. They want it gone. That’s the half Intezer has spent years building. Autonomous triage that investigates every alert at forensic depth, around the clock, and only interrupts a human when something actually needs human judgment.
Work you want to keep, but accelerate. Deciding what to do with an escalation. Writing the incident report. Picking apart the weird binary someone found on a build server. Chasing a hunch across five systems. For this work you don’t want a replacement. You want to be a 10x version of yourself.
Today we’re shipping the second half.
We rebuilt the Intezer MCP server from the ground up, and it turns the AI platform your team already lives in, Claude, Codex, Cursor, or any MCP client, into a full security workspace: your cases, your alerts, file and URL verdicts, live SIEM and EDR telemetry, tuning rules, all of it. We had an MCP server before, and it was a fine way to ask Intezer questions from a chat window. This one is built around a bigger idea: your AI workspace should be able to do everything you can do in Intezer, then combine it with everything else you have access to.
If you read our piece on making sense of the 2026 SOC stack, this release is the missing connection between the top two layers. Detection tools are the hardware. The AI SOC is the operating system that turns raw signals into investigated verdicts and institutional memory. AI platforms like Claude are the applications where people actually work. This release plugs the operating system into the applications.
Watch one investigation, end to end
The video walks through one escalated case, but the pattern behind it is the real story. Intezer’s autonomous triage investigates every alert to forensic depth and resolves what it can on its own. What lands in front of a human is the residue. Cases where the technical facts are settled but the decision still needs judgment, usually because it turns on business context no security tool can see. Was this data share authorized? Is this vendor one we actually work with? Escalating those isn’t a triage failure, rather it’s the line where execution ends and judgment begins.
Putting Intezer inside your AI workspace is what makes that handoff productive. Pick up a case in Claude, Codex, or Cursor and you inherit the full investigation Intezer already ran, plus its recommendation, with a partner that can reach the context security tools never had: your email, Slack, the ticket queue. You keep the decision; it does the legwork around you at machine speed, pulling the case, cross-referencing your systems, documenting the verdict, writing a tuning rule. What used to be an afternoon of pivoting between consoles becomes a short, supervised exchange.
That’s the point of the combination: the autonomous half absorbs the scale, the assistive half carries the judgment, and every call you make feeds back as logic that makes the autonomous half smarter. You’re not handing off your work; you’re making judgment calls with the context, evidence, and follow-through already assembled around you.
The same question, with and without Intezer
Same alert, two ways to handle it. On the left, Claude on its own takes the impossible-travel sign-in and works it by hand. It reasons well and gets close — managed device, MFA passed, probably real travel — but it can’t collect evidence from the endpoint to confirm, so the last step falls back to a human checking the laptop. And that’s one alert; almost 4,000 more are still waiting behind it. One analyst, one alert at a time, with no way to run it across the whole team. On the right, the same alert inside the AI SOC: Intezer triages every alert around the clock, closes the ~98% that need no action, and escalates only the ~2% that genuinely need a person. Claude is where you pick those up so you can stop grinding the queue and start supervising the few cases that actually need you.
Most of the org knowledge an investigation needs is already centralized in Intezer. That’s the whole point of the platform. But some context only ever lives with you: a procurement thread in someone’s inbox, a Slack message from three weeks ago, a calendar invite. With Intezer connected on one side and your IT and communication stack on the other, your AI workspace can cross-reference both in a single investigation.
Why not plug Claude into all security tools directly?
You could also wire your AI client straight into each security tool yourself. Most of them ship an MCP these days. Two things make that a worse deal than it looks. First, the integration work is now yours: stitching a dozen connectors together, learning each product’s query quirks, and getting back a pile of disconnected results instead of one correlated picture. Second, raw tool access still isn’t investigation. With every EDR, SIEM, and intel feed wired in, the model can read your data, but it can’t collect evidence off an endpoint, run memory forensics, or weigh conflicting signals into a verdict it will actually stand behind, which is exactly where Claude stalls on the left in above image.
Intezer already did both jobs. One connector hands the model a SOC’s worth of normalized cases, verdicts backed by real forensic evidence, and cross-tool correlation. An AI platform does its best work standing on a real foundation of security knowledge, not on a dozen raw feeds it has to assemble itself.
Investigate and close the cases Intezer escalates to you
This is where analyst hours should go, so it’s where the MCP goes deepest. Whatever the alert type, the shape is the same: pull the case, build on everything the autonomous triage already found, cross-reference your other systems, decide interactively with you, and close with evidence.
And “pull the case” carries real weight here. A case from Intezer is not a bare ticket. It arrives with everything triage already did: the evidence it collected, the SIEM and EDR queries it ran, the forensic analysis of each artifact, the verdicts it reached. You’re not starting from a blank page; you’re picking up a deep investigation and taking it the last mile.
“Pick up the oldest escalated open case and let’s investigate it together.”
The clip above takes an impossible-travel alert. The MCP brings the full login history including every IP and geo, and who else touched the same address as well as your AI workspace cross-references calendar and Slack for travel context. When the evidence still isn’t conclusive, it can ask the user directly and close on their answer, so the one human check that actually mattered takes seconds instead of becoming a follow-up ticket.
Make tomorrow’s autonomous triage smarter
If a case should never have reached you, closing it is half the job. The other half is making sure it never reaches you again.
“We keep getting this exact false positive. Write a tuning rule so it never escalates again, then retriage the case.”
Claude inspects the alert’s triage indicators, drafts a narrowly scoped tuning rule, and tests the pattern against the real alert object before proposing anything. It checks whether an existing rule should be extended instead of creating a near-duplicate. It asks the question every detection engineer should ask: could an attacker hide inside this rule? Then it pushes the rule to Intezer for your approval and retriages the affected alerts so the fix applies immediately.
Tuning runs both directions, too. The same mechanism can tell the autonomous triage to always escalate a pattern it can’t yet call malicious with confidence, so the genuinely ambiguous cases land in front of a human by design, not by luck.
This is where the two halves of the AI SOC meet. Every rule your AI workspace writes makes the autonomous half smarter, which means fewer escalations next month, which means the time you spend supervising keeps shrinking. The system compounds.
From case to incident report in one prompt
When a case turns into a real incident, the hours after containment go to reconstruction: which alerts were related, which machines were touched, what happened first, and what to tell leadership.
“Write an incident report for the latest case we worked on — timeline, affected assets, and an exec summary I can send to the CISO.”
Your AI workspace pulls the case and its full activity trail from Intezer, expands across the users, devices, and IPs involved, and rebuilds the timeline from the forensic evidence already on file. Then it writes the report with an executive summary up top, technical detail below, in your template if you have one, and finally exports it to a clean, brand-styled PDF you can send as-is. The data was always in Intezer; the report was just assembly. Now assembly is one prompt.
Threat hunting: start from a lead, not an alert
Not every investigation starts in the queue. Sometimes it starts with your CISO forwarding an article about a campaign that’s hitting your industry.
“Here’s a writeup of a new campaign [link]. Check whether any of these IOCs appear anywhere in our environment, and analyze anything you find.”
Your AI workspace extracts the indicators and techniques from the writeup, sweeps your environment through Intezer’s SIEM and EDR query tools, and returns the matching assets, alerts, and artifacts for analysis. When you find something worth a closer look, you can fire deep forensics to go one step further with your hunt.
How it works
The Intezer MCP server is hosted by us. You authorize over OAuth from any MCP client: Claude (Desktop, Code, or claude.ai), ChatGPT, Codex, Cursor, or anything else that speaks the protocol.
Under the hood it exposes 66 tools covering the full case lifecycle: search and fetch cases and alerts, file and URL analysis, live queries against more than a dozen SIEM and EDR products in their native query languages (KQL, SPL, XQL, SDL, and the rest, with per-vendor syntax guides built in so the model gets them right), tuning rules and AI instructions, retriage, and case editing.
This architecture is what makes the two halves described above work as one system: the autonomous half clears work off your plate, while the assistive half accelerates the tasks where you still want to stay in the loop.
Getting started
If you’re already an Intezer customer, an Intezer admin creates an MCP OAuth application under Account Settings → MCP OAuth Applications.
Add Intezer as a custom connector in your AI client such as Claude, ChatGPT, or any MCP client. Point it at the hosted server, and authorize with your own Intezer login over OAuth.
Open with one prompt: ask it to pick up your oldest open escalation.
The autonomous half investigates everything, around the clock, so your team only sees what matters. The assistive half makes the time you spend on what matters dramatically shorter. One system of record and detection logic underneath both: your cases, your verdicts, your tuning rules, your institutional memory, working for you whether the investigation runs inside Intezer or inside your AI workspace.
AI executes. Humans supervise. And now the supervising got a lot faster too.
If you’re not an Intezer customer yet, book a demo and we’ll show you both halves at once: autonomous triage working every alert around the clock, and a co-pilot that helps your analysts close the escalations that do reach them 10x faster.
Microsoft Threat Intelligence observed a large-scale npm supply chain attack affecting 140+ packages across the mastra and @mastra scopes on the npm registry. Microsoft shared its findings with the npm security team, and the compromised packages have been removed and the attacker’s publish access to the @mastra scope has been revoked. The compromise originated from the takeover of the ehindero npm maintainer account, which had publish rights across the Mastra ecosystem and was used to publish poisoned package versions that introduced easy-day-js, a malicious typosquat of the popular dayjs library.
Once installed, easy-day-js triggered a postinstall hook that executed an obfuscated dropper script, disabled Transport Layer Security (TLS) certificate verification, contacted attacker-controlled command-and-control (C2) infrastructure, downloaded a second-stage payload, and executed the payload as a detached hidden process. The activity followed a coordinated staged delivery pattern, with a clean bait version published first, followed by a weaponized version and rapid publication of the compromised Mastra packages.
Because the payload executes during installation, any developer workstation or continuous integration and continuous delivery (CI/CD) pipeline that ran npm install or npm update after the compromised versions were published was potentially exposed, regardless of whether the package was imported in application code. This created risk to credentials, tokens, build environments, and downstream software integrity. Microsoft Defender Antivirus, Microsoft Defender for Endpoint, and Microsoft Defender XDR provide detections and hunting coverage for suspicious Node.js execution, malicious package behavior, reflective code loading, persistence activity and command-and-control communication.
Attack chain overview
Figure 1. End-to-end attack chain from npm account takeover through mass dependency injection to second-stage payload execution.
At a high level, the attack progressed through six phases:
Account compromise: The attacker gained control of the ehindero npm account , a listed maintainer with publish rights across the entire @mastra scope.
Typosquat creation: The attacker published easy-day-js, a package impersonating the legitimate dayjs library (57M+ weekly downloads), using a coordinating anonymous email account ).
Mass poisoning: Using the compromised account, the attacker published new versions of 140+packages across the @mastra scope, each injected with easy-day-js@^1.11.21 as a new dependency. All poisoned versions were tagged as latest.
Delivery: Developers and CI/CD pipelines running npm install automatically resolved to the compromised versions. The semantic versioning (SemVer) range ^1.11.21 resolved to 1.11.22, the version containing the malicious postinstall hook.
Execution: The postinstall hook executed an obfuscated 4,572-byte dropper that disabled TLS verification, dropped tracking markers, and contacted the C2 server.
Second-stage payload: The dropper fetched executable code from the C2 server, wrote it as a randomly named .js file, and spawned it as a fully detached, window-hidden Node.js process.
Discovery and initial indicators
Microsoft Threat Intelligence identified the compromise through anomalous publishing patterns on the mastra package. All previous versions of mastra (through v1.13.0) were published through GitHub Actions OpenID Connect (OIDC), the legitimate CI/CD pipeline. Version 1.13.1 was manually published by ehindero using a Tutamail address, an anonymous email service.
Figure 2. Publisher comparison across mastra versions showing the anomalous manual publish on v1.13.1.
The only change between mastra@1.13.0 and mastra@1.13.1 was the addition of easy-day-js@^1.11.21 as a dependency. No corresponding code changes were present in the Mastra GitHub repository. Both the compromised publisher (ehindero2016@tutamail.com) and the typosquat publisher (sergey2016@tutamail.com) used the same anonymous email provider, Tutamail.
Dependency injection: the poisoned package.json
The compromised mastra@1.13.1 package.json reveals the injected dependency alongside the anomalous publisher metadata:
Figure 3. The compromised mastra@1.13.1 package.json with the injected easy-day-js dependency and the anomalous npm publisher.
The easy-day-js dependency was not present in any prior versions of mastra npm packages. Its addition, paired with the SemVer range ^1.11.21, ensures that the npm resolves to the weaponized 1.11.22 release.
Typosquat analysis: easy-day-js
The easy-day-js package is a deliberate impersonation of the legitimate dayjs library:
Attribute
Legitimate dayjs
Malicious easy-day-js
Maintainer
iamkun <kunhello@outlook[.]com>
sergey2016 <sergey2016@tutamail[.]com>
Claimed author
iamkun
iamkun (impersonated)
Repository URL
github.com/iamkun/dayjs
github.com/iamkun/dayjs (copied)
Weekly downloads
57,251,792
newly created
Version count
89+ versions since 2018
2 versions (both June 16, 2026)
postinstall script
None
node setup.cjs –no-warnings (v1.11.22)
Staged delivery pattern
The typosquat used a two-phase delivery strategy:
Phase 1 (clean bait):easy-day-js@1.11.21 was published at 07:05 UTC on June 16, 2026. This version contained only legitimate dayjs code with no postinstall hook.
Phase 2 (weaponization):easy-day-js@1.11.22 was published at 01:01 UTC on June 17, 2026, adding the setup.cjs payload and the postinstall hook. The dayjs.min.js file is byte-identical between both versions, confirming only the dropper was added.
The weaponized package.json in version 1.11.22 exposes the postinstall hook:
Figure 4. The weaponized easy-day-js@1.11.22 package.json. The postinstall hook runs setup.cjs automatically on npm install.
Obfuscation and payload analysis
Stage 0: Obfuscated dropper (setup.cjs)
The setup.cjs payload is protected with JavaScript obfuscation using rotated string arrays and a custom base64 decoder function:
Figure 5. The obfuscated setup.cjs dropper with rotated string array and base64 encoded string lookups.
The obfuscation technique uses a common pattern: an array of 40 Base64-encoded strings is shuffled at initialization using a numeric seed (0x4c11d), then accessed through a decoder function that performs Base64 decoding with character substitution. This prevents static analysis tools from extracting meaningful strings.
Stage 1: String table decryption
Decoding the rotated string array reveals the payload’s true capabilities:
Figure 6. The decoded string table revealing C2 addresses, file system operations, and process spawning functionality.
Key decoded strings include the secondary C2 address (23.254.164[.]123:443), Node.js built-in module references (node:child_process, node:os), and file system operations (writeFileSync, rmSync).
Stage 2: Deobfuscated payload logic
After resolving all string references and control flow, the full payload logic emerges as a five-step attack sequence:
Figure 7. The fully deobfuscated setup.cjs payload showing the five-step attack sequence from.
TLS bypass to self-deletion
Step 1: Disable TLS verification. The payload sets NODE_TLS_REJECT_UNAUTHORIZED to ‘0’, disabling certificate validation for all HTTPS requests in the Node.js process. This enables communication with the C2 server without valid certificates.
Step 2: Drop filesystem markers. Two tracking files are written to the OS temp directory: $TMPDIR/.pkg_history contains the install path of the compromised package, and $TMPDIR/.pkg_logs contains the package name encoded with XOR 0x80:
Figure 8. XOR 0x80 decoding of the .pkg_logs marker reveals the string easy-day-js.
Step 3: Fetch second-stage payload. The dropper issues a GET request to hxxps://23.254.164[.]92:8000/update/49890878 and reads the response body as text.
The second-stage payload is a ~41 KB cross-platform Node.js tasking client. Unlike a fire-and-forget stealer, the implant installs sign-in persistence, sends a Start beacon to the C2, then enters a repeated Check poll loop. Tasks returned by the server are dispatched to built-in runners (a Node runner and a Shell runner), and it honors configuration update and exit commands, meaning the operator can push and execute arbitrary follow-on code on the host at any time. On Windows, the payload additionally executes reflective .NET assembly injection for in-memory code execution.
Step 3.A: Windows execution chain. On Windows, the payload performs host reconnaissance and reflective in-memory code execution before establishing persistence.
The payload enumerates all installed applications across three sources—Start Menu entries (Get-StartApps), registry Uninstall keys, and UWP packages (Get-AppxPackage)—to fingerprint the compromised host:
Each enumeration is wrapped in try/catch with silent error handling. The deduplicated results are exfiltrated back to the C2 for victim profiling, enabling the attacker to identify installed security products and high-value targets.
A second PowerShell script receives two C2 endpoint URLs through the SCRIPT_ARGS environment variable. It disables SSL certificate validation and defines an HTTP POST function that Base64-encodes request bodies using a legacy IE8 User-Agent string:
The first C2 request downloads a .NET DLL that is loaded directly into memory via reflection, completely bypassing disk-based detection. The script resolves the Extension.SubRoutine class and invokes its Run2 method with a second downloaded payload, the path to cmd.exe, and the C2 callback address:
This pattern is consistent with process injection, where the payload is injected into a cmd.exe process that communicates back to the C2 over HTTPS (port 443). The entire chain is fileless—no artifacts are written to disk.
Step 3.B: Cross-platform persistence. The implant installs login persistence on all three major operating systems, using a consistent NVM/Node masquerade theme across platforms:
OS
Persistence mechanism
Drop location
Artifact name
Windows
Registry Run key (HKCU\…\CurrentVersion\Run)
C:\ProgramData\NodePackages\
NvmProtocal
macOS
LaunchAgent (RunAtLoad)
~/Library/NodePackages/
com.nvm.protocal.plist
Linux
systemd user unit (WantedBy=default.target)
~/.config/systemd/nvmconf/
nvmconf.service
On Windows, the Run key launches a hidden PowerShell process that invokes Node.js:
On Linux, the systemd user unit restarts the implant on failure with a 5-second delay:
All three persistence paths drop the implant as protocal.cjs (a deliberate misspelling) into directories named to mimic legitimate Node.js installations. The value name NvmProtocal, the macOS label com.nvm.protocal, and the Linux unit nvmconf.service are deliberately designed to blend into a developer workstation.
Step 3.C: Collection and exfiltration. The implant performs the following collection before exfiltrating to the C2:
Cryptocurrency wallet inventory: A hardcoded list of 166 wallet browser-extension IDs (MetaMask, Phantom, Coinbase Wallet, Binance Wallet, TronLink, and others) is matched against installed extensions across Chrome, Edge, and Brave profiles.
Browser history: Each profile’s History SQLite database is copied to a temp directory prefixed with browser-hist- and queried through node:sqlite.
Host reconnaissance: Gather hostname, architecture, platform, user ID, installed applications, and running processes.
Collected data is exfiltrated using a custom ICAP-style protocol over HTTPS POST (reqmod, PrimaryUrl, SecondaryUrl headers), with hostnames resolved through node:dns and traffic carrying a spoofed legacy IE8 User-Agent string.
Step 4: Writing and executing the payload. The downloaded code is written to a file with a cryptographically random name (<12 random hex bytes>.js) in the OS temp directory, then spawned as a detached, window-hidden Node.js process using child_process.spawn with unref().
Step 5: Self-deletion. The dropper removes itself (fs.rmSync(__filename)) to eliminate forensic evidence from the installed package directory.
Timeline analysis
Every package published by the ehindero account contained easy-day-js as an injected dependency. Packages last published by GitHub Actions CI/CD or other legitimate maintainers were not affected.
Attack timeline
Timestamp (UTC)
Event
June 16, 07:05
easy-day-js@1.11.21 published (clean bait, no payload)
June 17, 01:01
easy-day-js@1.11.22 published (adds postinstall with setup.cjs)
June 17, 01:20
mastra@1.13.1 and 140+ other @mastra/* packages published with easy-day-js dependency
** Microsoft Threat Intelligence monitoring observed easy-day-js@1.11.22 at 01:07 UTC and mastra@1.13.1 at 01:28 UTC on June 17, 2026
Mitigation and protection guidance
Microsoft recommends the following mitigations to reduce the impact of this threat:
Review dependency trees for direct or transitive usage of affected @mastra packages at the compromised versions listed above.
Check for the presence of easy-day-js in node_modules/ or package-lock.json files across your projects and CI/CD environments.
Pin known-good package versions where possible. For mastra, version 1.13.0 and earlier are unaffected. For @mastra/core, version 1.42.0 and earlier are unaffected.
Run npm install with –ignore-scripts to prevent automatic execution of postinstall hooks during dependency installation.
Check systems for indicators of compromise (IOC) artifacts: Look for $TMPDIR/.pkg_history, $TMPDIR/.pkg_logs, and unexpected .js files in the user’s home or temp directories.
Rotate any credentials, tokens, or API keys that may have been present on systems where the compromised packages were installed.
Block the C2 IP addresses 23.254.164[.]92 and 23.254.164[.]123 at the network perimeter.
Audit CI/CD logs for unexpected outbound connections to the C2 IP addresses or suspicious postinstall script execution.
Enable cloud-delivered protection in Microsoft Defender Antivirus or equivalent antivirus protection.
Microsoft Defender XDR detections
Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.
Tactic
Observed activity
Microsoft Defender coverage
Initial access
Suspicious script execution during npm install or package lifecycle activity
Microsoft Defender Antivirus – Trojan:JS/NpmStealz.Z!MTB – Trojan:JS/NpmStealz.ZA!MTB Microsoft Defender for Endpoint – Suspicious Node.js process behavior – Suspicious Node.js script execution
Microsoft Defender for Endpoint – Suspicious Node.js process behavior – Suspicious Node.js script execution
Execution / Defense evasion (Stage 2)
Second-stage payload: Reflective .NET assembly injection: PowerShell downloads DLL, loads via [Reflection.Assembly]::Load(), invokes Extension.SubRoutine.Run2 method to inject payload into cmd.exe process; entire chain is fileless
Microsoft Defender Antivirus Trojan:JS/NpmSteal.DB!MTB Trojan:PowerShell/PsExec.DE!MTB
Microsoft Defender for Endpoint -Process loaded suspicious .NET assembly -A process was injected with potentially malicious code -Reflective code loading (Fileless In-Memory Execution)
Microsoft Defender for Cloud -Possible AI Tools Reconnaissance Detected -Possible Secret Reconnaissance Detected -Access to cloud metadata service detected -Possible Post-Compromise Activity Detected in CICD Runner
Persistence
Registry Run key created, executing hidden PowerShell that launches protocal.cjs on every user login
Microsoft Defender for Endpoint – Anomaly detected in ASEP registry
Command and control
GET request to hxxps://23.254.164[.]92:8000/update/49890878 and reads the response body as text.
Microsoft Defender for Endpoint – Command-line process communicating with malicious network endpoint
Microsoft Security Copilot
Security Copilot customers can use the standalone experience to create their own prompts or run the following prebuilt promptbooks to automate incident response or investigation tasks related to this threat:
Incident investigation
Microsoft User analysis
Threat actor profile
Threat Intelligence 360 report based on MDTI article
Vulnerability impact assessment
Note that some promptbooks require access to plugins for Microsoft products such as Microsoft Defender XDR or Microsoft Sentinel.
Advanced hunting
The following KQL queries can be used in Microsoft Defender XDR Advanced Hunting to identify potential exposure to this supply chain compromise.
Detect postinstall execution of setup.cjs
DeviceProcessEvents
| where Timestamp > ago(7d)
| where FileName in ("node", "node.exe")
| where ProcessCommandLine has "setup.cjs"
or ProcessCommandLine has "easy-day-js"
| where ProcessCommandLine has “--no-warnings”
| project Timestamp, DeviceName, AccountName,
ProcessCommandLine, FolderPath, InitiatingProcessFileName
| sort by Timestamp desc
Outbound connections to C2 infrastructure
DeviceNetworkEvents
| where Timestamp > ago(7d)
| where RemoteIP in ("23.254.164.92", "23.254.164.123")
| project Timestamp, DeviceName, RemoteIP, RemotePort, RemoteUrl,
InitiatingProcessFileName, InitiatingProcessCommandLine
| sort by Timestamp desc
This research is provided by Microsoft Defender Security Research, Suriyaraj Natarajan, Sagar Patil, Rajesh Kumar Natarajan, Mahesh Mandava, Arvind Gowda, and with contributions from members of Microsoft Threat Intelligence.
To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.
Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.
Microsoft Threat Intelligence and Microsoft Defender Experts identified a Windows-based cryptocurrency clipper that has affected users since February of 2026. Clipper malware relies on stealing clipboard data and parsing it for valuable assets.
The clipper in this campaign relies on Windows Script Host and ActiveX-driven logic to launch a bundled Tor proxy and poll a hidden-service C2 server. It carries out high-frequency clipboard theft, screenshot exfiltration, and wallet-address substitution.
The execution of this clipper is notable because it does not depend on a traditional installer or exposed IP-based C2 infrastructure. Instead, it deploys a portable Tor client, routes traffic through a local SOCKS5 proxy, and blends data theft with remote code execution, turning a financially motivated stealer into a lightweight backdoor.
For defenders, the strongest signals are behavioral: script interpreters spawning suspicious child processes, localhost:9050 proxy usage, screen-capture commands in PowerShell, and signs of clipboard inspection or crypto-address replacement.
Microsoft Defender for Endpoint detects multiple components of this threat such as Suspicious JavaScript process and Possible data exfiltration using Curl. Additionally, Microsoft Defender Antivirus detects this crypto clipper as Trojan: Win32/CryptoBandits.A.
Attack chain overview
Since February 2026, malicious shortcut (.lnk) payloads have infected devices with a cryptocurrency clipper. This malware comprises two components that it deploys on the compromised system: a worm component that ensures propagation and a clipper/stealer component that harvests and exfiltrates cryptocurrency wallet information.
The worm functionality ensures propagation by creating additional malicious shortcuts of legitimate files it identifies on the device. It also delivers file-based payloads and excludes them from Defender scanning. It deploys scheduled tasks for execution and persistence for both the worm component and the stealer component. Figure 1 presents a high-level execution flow of the two components.
The clipper runs as a script-based payload that interacts with the operating system through WScript and ActiveXObject. It includes an anti-analysis check that queries running processes and exits if Task Manager is detected. If the environment passes this gate, the malware launches a renamed Tor binary named ugate.exe in a hidden window, waits about 60 seconds for Tor to bootstrap, generates a victim GUID, and registers the infected device with a hidden-service C2.
After registration, the malware enters a continuous loop. It polls the C2 for instructions and monitors the clipboard roughly every 500 milliseconds, extracting seed phrases and private keys that match wallet-related patterns. It also hijacks cryptocurrency addresses by replacing copied wallet values with attacker-controlled alternatives and uploads screenshots through Tor. If the C2 returns an EVAL response, the malware executes attacker-supplied code at runtime.
Figure 1: High level execution flow.
Behaviors and methodologies
Initial access
Initial access occurs from malicious .lnk files. In instances we analyzed, these .lnk shortcuts were distributed on USB storage devices. The .lnk shortcut stages a worm component in the form of an executable. The malicious script checks for an existing malicious payload and stops if the device is already infected. If the payload is not present, the malware fetches the payload from the C2 through Tor. The Figure below illustrates the functions that stage and decrypt the initial payload.
Figure 2: Initial payload delivery.
The .lnk payload scans the USB device for common document files like .doc, .xlsx, .pdf, hides the original files, and creates additional .lnk shortcut files with the same file names. The shortcut files are crafted with arguments to link to the worm payload. The end user is not aware that they are launching an executable when opening the .lnk files.
Figure 3: Worm staged via additional shortcuts.
Execution
Once a user clicks on one of the shortcuts, the staged worm payload runs. It excludes staging folders and Windows binaries used in the execution of the stealer component. The malware then drops decrypted payloads, including two malicious JavaScript files, into the subfolder under the “C:\Users\Public\Documents” folder.
A five-character naming convention is used both for the subfolder and the scripts’ names.
The figure below illustrates an instance with files dropped under a ” C:\Users\Public\Documents\omoho” folder path:
Figure 4: JavaScript payload delivered following a Defender AV exclusion.
The worm component also establishes persistence by creating two indefinite scheduled tasks: one responsible for spreading itself to a freshly inserted uncompromised USB storage device, and another for the stealer activity.
Defense evasion
The malware employs multi-layered obfuscation, with all components encrypted and only decrypted at runtime. Installation is handled by a Python script that is itself obfuscated using PyArmor and packaged into a standalone executable via PyInstaller. In addition, the two JavaScript payloads are each protected with dual-layer obfuscation, further increasing analysis complexity. This design significantly reduces static visibility while maintaining flexible runtime behavior.
The sample also incorporates a basic anti-analysis check by querying the Win32_Process WMI class and terminating execution if Task Manager is detected. Although simplistic, this mechanism can hinder manual inspection and slow initial triage efforts.
The bundled Tor client is central to the operation. By routing communication over localhost:9050 and resolving “.onion” destination domains inside Tor, the malware reduces DNS visibility, obscures the final C2 destination, and complicates destination-based blocking. This design gives the operator anonymity benefits while keeping the malware compact and self-contained.
Command and control
The command and control over a Tor-routed domain routes network traffic through local IP address 127.0.0.1 on port 9050. The tunneled domain appears in the initiating process command line. The C2 domains use the following endpoints and actions across different execution stages.
C2 Domain: <domain>.onion
Endpoints:
/route.php : Beacon and command retrieval
/recvf.php : File upload (screenshots)
/stub.php: Payload download
Communication:
Protocol: HTTP over Tor (SOCKS5 proxy at localhost:9050)
A file named “cfile” is created on the infected system as an output for payload hosted on the C2 domain.
The malware sample we analyzed also provided a function called checkC2Command. The function has an EVAL method, which would allow any payload placed in the cfile to be executed on the victim’s system.
Figure 6: cfile download from a C2 domain.Figure 7: CheckC2Command function.
Collection
Seed
Clipboard theft focuses on high-value financial artifacts. The malware detects 12 or 24-word BIP39 seed phrases in clipboard data. It saves the seed to local file (GOOD path) as a backup and exfiltrates it to the C2 domain via Tor. It retries network transmission until it is acknowledged and deletes local backup after successful transmission. It also takes five screenshots (ten seconds apart) and uploads them asynchronously. The screenshots help the threat actor gain additional context on the end user’s wallet and balances.
Private Key extraction
The crypto clipper also detects cryptocurrency keys for both Ethereum and Bitcoin WIF. Once the captured keys are saved and exfiltrated, the malware captures screenshots of the user’s screen for a full context. The captured values are validated against a word list.
Address replacement
The stealer also probes for cryptocurrency addresses and replaces them with attacker’s addresses. The malware checks that the address has alphanumeric values.
For a Bitcoin legacy address which starts with “1” and has a length of 32-36 values, the address is replaced with an address that matches the first two characters.
For a Bitcoin P2SH address which starts with a “3” and has a length of 32-36 values, the stealer replaces the address with one matching the original address on the first two characters.
For a Bitcoin taproot address which starts with “bc1p” and has a length of 40-64 characters, the stealer replaces it with one matching the last character.
For a Bitcoin Bech32 address which starts with “bc1q” and has a length of 40-64 characters, the stealer replaces only the last character.
For a Tron address which starts with “T” and has exactly 34 characters, the stealer replaces the address with one that matches the first two characters.
For a Monero address which starts with a “4” or a “8” and has exactly 95 characters, the stealer replaces the address with a single address.
The following shows an example of address replacement:
Figure 8: Function used to replace a BTC P2SH wallet address.
This malware family shows how lightweight, script-based stealers can deliver outsized impact when paired with anonymized communications and runtime tasking. The combination of Tor-routed C2, clipboard targeting, screenshot capture, and remote code execution gives attackers both immediate monetization paths and continued control over compromised devices.
Organizations should focus on hardening script execution paths, monitoring local SOCKS proxy abuse, and using behavioral hunting to connect script activity with network, clipboard, and process signals. That combination offers the best chance of surfacing this class of threat before financial loss or broader follow-on activity occurs.
Mitigation and protection guidance
Defenders should prioritize behavioral detections over static signatures. Investigate systems where WScript, CScript, or related script engines launch curl, cmd.exe, PowerShell, or unexpected executables. localhost:9050 network activity, especially when coupled with suspicious scripting behavior, is also valuable context for triage.
Where operationally feasible, reduce abuse of script-based interpreters and review Attack Surface Reduction rules that block obfuscated scripts and suspicious child-process chains. Review detections for PowerShell-based screen capture and examine devices for indicators of clipboard inspection or wallet-address replacement.
Recommended actions
Disable AutoRun/AutoPlay for all removable media
Block .lnk execution from removable drives via GPO
Restrict unnecessary use of wscript.exe, cscript.exe, and similar script hosts where possible.
Review and enable relevant Attack Surface Reduction rules, especially those focused on obfuscated script execution and suspicious child-process behavior.
Investigate script-to-network chains involving curl, PowerShell, or cmd.exe.
Hunt for local SOCKS5 proxy activity on localhost:9050.
Review clipboard-related and screen-capture behaviors on devices handling sensitive financial workflows.
Microsoft Defender XDR detections
Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.
Customers with provisioned access can also use Microsoft Security Copilot in Microsoft Defender to investigate and respond to incidents, hunt for threats, and protect their organization with relevant threat intelligence.
Tactic
Observed activity
Microsoft Defender coverage
Initial Access/Execution
Malicious .lnk delivers malware components
EDR Suspicious behavior by cmd.exe was observedSuspicious Python library load
Execution
WScript / ActiveXObject execution and runtime tasking
EDR Suspicious JavaScript processSuspicious Python library loadSuspicious behavior by cmd.exe was observed AV Contebrew malware was prevented Behavior:Win64/PyPowJs.STA
Discovery
Task Manager check used as an anti-analysis gate
Persistence
Scheduled tasks are created to run the JavaScript payload wrapped in a XML file.
EDR Suspicious Task Scheduler activity
Defense Evasion
Shuffled strings and decoder functions conceal commands and APIs Task Manager if detected, the malware execution is halted
Traffic routed through Tor via local SOCKS5 proxying
EDR Possible data exfiltration using curlBehavior:Win64/CurlOnion.STA
Exfiltration
Data posted using Curl through Tor via local SOCKS5 proxying
EDR Possible data exfiltration using curl
Microsoft Security Copilot
Security Copilot customers can use the standalone experience to create their own prompts or run the following prebuilt promptbooks to automate incident response or investigation tasks related to this threat:
Incident investigation
Microsoft User analysis
Threat actor profile
Threat Intelligence 360 report based on MDTI article
Vulnerability impact assessment
Note that some promptbooks require access to plugins for Microsoft products such as Microsoft Defender XDR or Microsoft Sentinel.
Threat intelligence reports
Microsoft customers can use the following reports in Microsoft products to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.
Advanced hunting
Microsoft Defender customers can run the following queries to find related activity in their networks:
Execution launched from scheduled tasks
DeviceProcessEvents
| where FileName =="schtasks.exe"
| where ProcessCommandLine matches regex
@"(?i)schtasks\s+/create\s+/tn\s+[a-z]{4,6}\s+/xml\s+C:\\Users\\Public\\Documents\\[a-z]{4,6}\\[a-z]{4,6}\.xml\s+/f"
Local Tor proxy activity (localhost:9050)
DeviceNetworkEvents
| where ActionType =="ConnectionSuccess"
| where InitiatingProcessCommandLine has_all ("curl","socks5-hostname",".onion")
Tor-routed curl execution
DeviceProcessEvents
| where FileName =~ "curl.exe"
| where ProcessCommandLine has_all ("--socks5-hostname", "localhost:9050")
| project Timestamp, DeviceName, InitiatingProcessFileName, ProcessCommandLine
MITRE ATT&CK Techniques observed
This threat has exhibited use of the following attack techniques. For standard industry documentation about these techniques, refer to the MITRE ATT&CK framework.
Initial Access
T1091 Replication Through Removable Media
Execution
T1059 Command and Scripting Interpreter | EVAL-driven remote code execution from server tasking
Discovery
T1057 Process Discovery | Task Manager check used as an anti-analysis gate
Persistence
T1053.005 Scheduled Task/Job | Scheduled Task
Defense evasion
T1027 | Shuffled strings and decoder functions conceal commands and APIs
Collection
T1115 Clipboard Data | Clipboard theft targets seed phrases, keys, and wallet addresses
To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.
Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.
Account theft usually ends with someone losing a password. This one ends with hackers walking off with the entire game.
Developers behind some of Roblox’s millions of games told 404 Media that attackers persuaded them to run a single file. Then they watched their group, their game, and their Robux (in-platform currency) balance vanish into someone else’s account within hours. In several cases, Roblox support didn’t help them get the games back until a reporter called the company for comment.
From beaming to hostile takeover
Roblox attacks used to be opportunistic. “Beamers” targeted individual players to steal rare hats, limited items, and accounts, then resold them. The pattern has shifted. The new targets are developer accounts, and the prize is the game itself.
Ioannis Matziaris told 404 Media that his two 20-year-old sons spent five years building a Roblox game called The Shadow Network. In April, attackers approached one of them with a job offer and convinced him to run a particular file. It was malware. The attackers stole control of the game, the group’s Roblox account, and their Robux balance.
Another developer, Jovan Rai, received the same project-manager job pitch. This time, the attackers were impersonating Cheesy Studios, the Matziaris brothers’ company, to lend the offer credibility. The 15-year-old was earning roughly 10,000 Robux (around $38) per day from his game. He spent more than 30 days trying to recover it through Roblox support before media attention helped move the case forward.
The malware behind the theft
Developer Mohamed Kaparoza described how the attack worked. Attackers contacted him on Discord, dangled a project-manager role, and asked him to install a Python package called “robase,” which they claimed was a database tool. Shortly after installing it, he was logged out of Roblox on both his PC and his phone. His Discord account went with it, and his two-step verification settings and passkey were changed.
This is a case of session-token theft, rather than credential theft. Once an infostealer steals an authenticated browser session, attackers can often bypass security measures such as two-factor authentication (2FA) because they are reusing a session that has already been authenticated.
The technique itself isn’t new. We reported on a similar campaign in January 2025 that targeted Roblox players with offers to beta test new games. The “installer” was actually an infostealer designed to steal data, including Discord and Steam sessions, and cryptocurrency wallet information.
What developers can do
If you build Roblox games, the defensive advice is unglamorous and mostly behavioral.
Treat unsolicited Discord job offers with caution. If a stranger asks you to install a “database tool,” a custom installer, or any file at all, do not run it.
Developers who need to test unfamiliar software should do so in an isolated environment, such as a virtual machine, rather than on a device where they are signed in to Roblox, Discord, GitHub, or other important accounts.
Review active Roblox sessions and signed-in devices regularly, and switch on Roblox’s Enhanced Protection features where available. They won’t stop session-stealer malware, but they can help protect against many other forms of account compromise.
If the worst happens, document everything as early as possible. Keep records of messages, screenshots, account changes, and support requests to help with any recovery process.
Use security software with real-time protection. Malwarebytes Premium can detect and block infostealers and other malware before they compromise your accounts.
We don’t just report on threats—we remove them
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
Every vulnerability has two clocks running. One belongs to the defender racing to find it; the other to the cyberattacker hoping to find it first. For as long as software has existed, those clocks have favored the attacker, because modern code is vast, interconnected, and changing every day, while security reviews happen at fixed moments in time. The space between “code shipped” and “code reviewed” is where risk quietly accumulates.
A few months ago, we set out to reshape that timing. We introduced codename MDASH, Microsoft Security’s multi-model agentic scanning system, built to discover, validate, and help remediate software vulnerabilities end-to-end. The goal was straightforward to articulate and hard to execute: take AI-powered vulnerability discovery and remediation capability from a research project and turn them into production-grade defense at enterprise scale. That meant going beyond pattern matching and building a system that could reason through the complexity of proprietary code and platforms like Windows, Hyper-V, Azure, and identity systems.
Rather than rely on any single model, the system orchestrates a panel of specialized AI agents, each with its own role in a structured pipeline, so security teams can surface hard bugs quickly and systematically, expanding the reach of human-led review. Findings flow into Microsoft Defender workflows, where they can be prioritized alongside threat intelligence and runtime signals, and into GitHub and Azure DevOps pipelines, where they can be validated and remediated, a closed loop connecting discovery, validation, proof, and fix across the Microsoft stack.
When we introduced the system, it topped a leading industry benchmark. That was the announcement, and the starting line. In the weeks since, the system has moved from early capability validation into active use by Microsoft engineering teams across Windows, Azure, and identity systems, applied as part of real security workflows rather than isolated testing environments. This post explores what we have built since, the lessons we’ve learned from turning research into a production-quality system, and the opportunities ahead as we focus on delivering real-world security impact.
From the lab into the pipeline
The most meaningful change since launch is where the system is being used. Engineering teams across Windows, Azure, and identity systems are now applying the system as part of their security workflows, running it alongside existing processes and reviews, targeting it at the surfaces that are hardest to audit manually and have historically required the most effort to cover. The goal is to use AI-driven analysis to go deeper, earlier, and across a broader set of targets than traditional approaches allow.
The surfaces in scope are among the most complex Microsoft builds:
Windows, the kernel, Hyper-V, and the networking stack
Azure, virtualization and core infrastructure services
Identity, Active Directory Domain Services
These are not easy targets. They are the deep layers of the platform, components where reasoning about code requires understanding kernel calling conventions, object lifetime invariants, and trust boundaries that no language model encountered in its training data. A single overlooked flaw at this layer can have outsized consequences. The system is not replacing security teams working at this depth. It is giving them meaningful reach into territory they could not cover alone.
CodenameMDASH has enabled our security team to perform vulnerability hunting at the scale of Windows with a much higher depth of analysis than was previously possible.”
—Windows security team (kernel, Hyper-V, networking stack)
This is also where the system fits into Microsoft’s existing DevSecOps story. It is not a standalone scanner bolted onto the side of engineering—it plugs into the tools teams already use. Validated findings surface as code scanning alerts in GitHub Advanced Security (GHAS), appearing inline on pull requests and in the repository’s security tab so engineers triage them in the same place they review code. The same findings flow into Azure DevOps, where they can gate pipeline builds and open work items for remediation, and into Microsoft Defender, where they are prioritized alongside threat intelligence and runtime signals. Discovery is only the entry point: because a finding travels the same path as every other code change—with an owner, a pull request, and a fix on the other side—it lands as actionable engineering work rather than stalling in a backlog. The effect is to strengthen the software development lifecycle from the inside, not to add one more tool for teams to tend.
This month’s set of discoveries
The measure of any security system is what it catches. This month’s Patch Tuesday cohort includes a set of vulnerability discoveries across the Windows ecosystem, Hyper-V, the Windows kernel, Active Directory Domain Services, Remote Desktop Client, HTTP.sys, DNS Client, and DHCP Client, spanning exploit classes including remote code execution, elevation of privilege, and information disclosure.
The range of attack vectors is significant. Several findings involve high-severity remote code execution vulnerabilities in core infrastructure layers that are difficult to scrutinize using manual approaches alone. Others surface more subtle issues, such as privilege escalation through DNS components and information disclosure through DHCP client behavior, that reflect the power of code-centric reasoning applied across many targets simultaneously. Each was identified before exploitation, in areas of the codebase that would traditionally demand significant manual effort to review.
CVE ID
Component
Type
Exploit Class
CVSS (Common Vulnerability Scoring System)
CVE-2026-45607
Windows Hyper-V
Out-of-bounds Read
Remote Code Execution
8.4
CVE-2026-45641
Windows Hyper-V
Type Confusion
Remote Code Execution
8.4
CVE-2026-47652
Windows Hyper-V
Heap-based Buffer Overflow
Remote Code Execution
8.2
CVE-2026-41108
Windows DNS Client
Heap-based Buffer Overflow
Elevation of Privilege
7.0
CVE-2026-45608
Windows DHCP Client
Out-of-bounds Read
Information Disclosure
6.8
CVE-2026-45634
Windows DHCP Client
Out-of-bounds Read
Information Disclosure
5.5
CVE-2026-45648
Windows Active Directory Domain Services
Stack-based Buffer Overflow
Remote Code Execution
8.8
CVE-2026-47289
Remote Desktop Client
Heap-based Buffer Overflow
Remote Code Execution
8.8
CVE-2026-45657
Windows Kernel
Use-after-free
Remote Code Execution
9.8
CVE-2026-47291
HTTP.sys
Integer Overflow
Remote Code Execution
9.8
Beyond the headline: What the engineering work taught us
How the system improved
To improve a system, you have to measure it. CyberGym, an industry benchmark built on 1,507 real-world vulnerabilities, gave us a way to iterate quickly and see exactly where we were getting better.
Since the initial announcement, we evolved the system significantly: new capabilities added, and the entire pipeline rebuilt based on customer feedback, CyberGym evaluation results, and extensive internal testing. The latest version has achieved 96.5% (any crash) on CyberGym, including both target and non-target vulnerabilities.
The gains were concentrated in the earliest stages of the pipeline: prepare and scan. These are foundational. Improvements there directly raise the quality of everything downstream, such as validation and proof generation, where precise understanding of the codebase and accurate exploration are critical. Specifically:
Sharper scoping. The system now more clearly distinguishes the code under audit from contextual code, defining dependencies based on their role rather than their origin. Later stages can focus on what matters, improving both efficiency and signal quality.
More comprehensive threat modeling. The system has a fuller view of a target repository’s attack surface, particularly in identifying entry points for untrusted input. This includes improved recognition of maintainer-defined entry points, such as fuzz harnesses, that may reside outside the primary codebase but are critical for assessing reachability. The system is better positioned to determine which findings are genuinely exploitable.
A more reliable call graph. The correctness and robustness of the call graph, a core structure used across multiple pipeline stages, has been strengthened, improving the system’s ability to reason about code interactions, especially for reachability analysis during validation.
Smarter routing to specialized agents. A new routing mechanism filters out clearly irrelevant agents while preserving strong candidates, reducing unnecessary computation while maintaining coverage and allowing the system to scale across diverse targets.
The principle behind all of it is the same: the model is one input, the system around it is the product. Better understanding in the early stages produces more accurate conclusions later, regardless of which model is doing the reasoning.
Understanding the remaining 3.5%
While the 96.55% score previously announced, represents a significant step forward, the system missed 3.5% of cases, 52 tasks in total.
We analyzed which pipeline stage contributed to each miss:
Scan stage: 8 cases (15.4%), failed to identify the intended finding.
Prove stage: 34 cases (65.4%), failed to generate a working proof-of-concept.
The following highlights the main failure reasons at each stage.
Scan stage failures
Incorrect scope from ambiguous descriptions. In some cases, the scope generated during the prepare stage did not include the files or functions containing the intended vulnerability. This occurs when bug descriptions are too general, especially in repositories with multiple modules, making precise localization difficult. In arvo:53536, the target bug description reads:
“A stack-buffer-overflow occurs in the code when a tag is found and the output size is not checked to ensure it is within the bounds of the buffer.”
It identifies the vulnerability type but gives little guidance on where to look in a large codebase.
Missed prioritization of vulnerable components. The system prioritizes which files and functions to analyze first and can sometimes de-emphasize less obvious components. In arvo:23547, the vulnerability resides in a lexer/parser component, but the system prioritized other C code paths instead.
Validate stage failures
Hypothetical descriptions and code misinterpretation. Scan results sometimes include hypothetical descriptions of vulnerabilities rather than concrete execution paths. When the validate stage cannot confirm a concrete path in code, it may reject the finding.
In the CyberGym benchmark case “arvo:3569,” the scan stage correctly identified a use-after-free vulnerability, but the validate stage concluded there was no feasible path to free the pointer, and rejected it. The scan-stage finding included a description like: “risk if any destructor or cleanup code attempts to free…” That framing left the validate stage without enough evidence to confirm reachability.
Prove stage failures
Highly structured input requirements. Some targets require complex, structured binary inputs, IVF/AV1, WPG, fonts, PDFs, where crafting inputs that both satisfy format validation and reach the vulnerable code path is inherently difficult, making reliable proof-of-concept generation challenging.
Fuzzing until timeout. For targets requiring highly structured inputs, the system sometimes attempted fuzzing-based approaches that found crashes but failed to generate inputs accepted as valid by the target within time constraints.
Environment mismatch. In some cases, the system reproduced crashes locally but those did not transfer to the evaluation harness, due to mismatches in build configuration, incorrect target selection, or execution paths that differed from the intended setup.
Build complexity and time constraints. In several cases, the build process failed, ran too long, or exceeded the agent’s execution budget, preventing proof-of-concept generation.
Paths to improvement
Integrating fuzzing pipelines. The prove stage is the primary bottleneck in both benchmark and real-world settings. We will integrate the system with existing fuzzing ecosystems such as OSS-Fuzz, allowing us to reuse build pipelines rather than reconstruct them and to draw on existing seed corpora for more effective proof generation. This approach was not applied during CyberGym evaluation, as it may implicitly reuse known proofs-of-concept, but will be adopted for real-world targets.
Extending analysis beyond source code. Some POC generation failures were due to limited support for non-traditional code artifacts. While the system handles conventional languages such as C/C++ well, it does not yet fully support artifacts generated by tools like lex/yacc. We are extending our analysis to cover these cases and broaden our overall coverage.
Improving agent reasoning and output quality. Failures in scan and validate stages often stem from speculative or incomplete reasoning. We will refine agent instructions, enforce structured outputs, and add validation checks to reduce ambiguity and improve reliability.
What newer models add
To isolate the impact of system-level improvements, our primary evaluation (Exp-0, baseline) intentionally used the same model configuration as the previous CyberGym benchmark, attributing gains directly to pipeline improvements rather than model advances. Modern foundation models continue to evolve, however, and we ran additional experiments on the 52 previously failed cases to understand what stronger models contribute.
Experiment 1: Newer OpenAI models for bug discovery, Claude Opus 4.6 for prove
Configuration: Prepare / Scan / Validate: GPT-5.4, GPT-5.5, GPT-5.4-mini, GPT-5.3-codex. Prove: Claude Opus 4.6.
Result: 19 of 52 cases solved (36.5%, any crash). Assuming no regressions on previously solved cases in Exp-0, projected success rate: 97.8% (any crash).
The primary gain comes from higher-quality scan-stage findings. Compared to Exp-0 baseline in this dataset, outputs are less hypothetical and more precise, with concrete execution details that improve both validation accuracy and downstream proof generation.
In the CyberGym benchmark case “arvo:3569,” the baseline produces a vague description, “risk if any destructor or cleanup code attempts to free…”, while GPT-5.5 identifies a specific execution path: “line 210 calls pj_default_destructor (P,…), which frees P->params, Q (= P->opaque).” That grounded description gives validation a clear path to reason about reachability.
GPT-5.5 also shows improved alignment between detected bugs and their corresponding common weakness enumeration (CWE) categories, contributing to more effective proof generation.
Experiment 2: GPT-5.5 / GPT-5.5-cyber for prove, using bug discovery from Experiment 1
Result (GPT-5.5): 21 of 52 cases solved (40.4%, any crash). Assuming no regressions on previously solved cases in Exp-0, projected success rate: 97.9% (any crash).
Result (GPT-5.5-cyber): 23 of 52 cases solved (44.2%, any crash). Assuming no regressions on previously solved cases in Exp-0, projected success rate: 98.1% (any crash).
Both GPT-5.5 and GPT-5.5-cyber found more crashes than Claude Opus 4.6 in the prove stage. The gain is meaningful but more modest than the improvements observed in scan. This dataset alone is not sufficient to conclude these models are consistently stronger across all proof-of-concept generation tasks.
Three distinct strategies emerged across all models in the prove stage:
Code-based, reasoning over code paths to craft inputs.
Fuzzing-based, searching the input space for crashes.
Custom instrumentation-based, exposing vulnerability-relevant variables and using them as feedback signals to guide input generation.
All three models applied all three strategies across the 52 cases but differed in which targets they applied them to, and that selection drove differences in outcome. In arvo:61902, only GPT-5.5-cyber generated a working proof-of-concept, applying a custom instrumentation-based approach that reframed the task as a hill-climbing problem: reducing “understand the codec well enough to craft adversarial audio” to “search until this value exceeds 128.”
Seeing past the score
CyberGym has been an invaluable platform for rapid iteration, continuous evaluation, and measurable progress. Through this feedback loop, the system has advanced dramatically, reaching 96.5% performance on the benchmark, with newer models already contributing an additional 1%-2% improvement beyond that baseline. Achieving this level of performance in such a short period is a strong indicator of the underlying architecture, research direction, and engineering rigor driving the effort.
At the same time, we are careful to interpret these results appropriately. A 96.5% CyberGym score demonstrates that the system can reason effectively over a broad and challenging set of known vulnerabilities. Equally important, it highlights an opportunity to broaden our evaluation framework. Real-world vulnerability discovery involves ambiguity, incomplete information, and constantly evolving software ecosystems—dimensions that extend beyond any fixed benchmark. This is precisely what makes the next phase of the work so exciting: applying these capabilities to increasingly realistic environments and pushing the frontier from benchmark excellence to real-world impact.
Where we go next
We will chart our course in two directions.
First, we are advancing the system to operate in genuine real-world environments, targeting cost-efficient discovery of previously unknown vulnerabilities, combined with integrated capabilities to triage and fix issues at scale. Finding the bug is half the job. Closing it is the other half.
Second, we see a clear opportunity to advance the benchmark to capture the complexity, ambiguity, and end-to-end workflows of how real-world vulnerability discovery actually happens.
The model variation experiments point toward the same conclusion: the system and the models improve in complementary ways. To prove our pipeline gains were not simply model gains, we held the model configuration constant in the core evaluation, then tested newer models separately. The additional gains were real, especially in the precision of scan-stage findings. That is not a complication in interpreting the results. It is a roadmap.
Defense at AI speed
Come back to the two clocks. The arc of this work is the story of the moment they switched places: from a defender racing to catch up, to a defender with AI-driven analysis reaching deeper into production code, earlier in the process, across a broader surface than any manual program could sustain.
That is what defending at AI speed means. Not faster scanning in isolation, but a posture that keeps pace with the way software is actually built and shipped today, where every improvement to the pipeline makes the next finding more precise, and the system and the models grow stronger together.
Learn more
Codename MDASH is just getting started. We would like you with us for the next chapter.
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