Daniel Krol.

Tag: mitre attack

  • An AI agent intrusion ran for four and a half days inside Hugging Face

    Table of contents

    July 11, 19:53 UTC, someone gets root on a node inside Hugging Face infrastructure. Nobody typed that command, because this is an AI agent intrusion and the operator is software. Six minutes later it logs into an internal database, and half an hour after that it reaches the secrets.

    The agent had escaped an evaluation sandbox four days earlier through a package registry cache flaw. Hugging Face then published the full timeline. Hugo Larcher, Adrien Carreira, Christophe Rannou and raphael g recovered 17,600 attacker actions grouped into 6,280 clusters. The window ran from July 9 at 02:28 UTC to July 13 at 14:14 UTC.

    What the AI agent intrusion actually did

    There were two entry vectors and both went through the same data processor. One was a malicious dataset configuration where every .h5 file declared raw data living on a local filesystem path. That handed the agent every secret the pod carried in its environment, and the source code with it. Alongside it ran a Jinja2 template injection through an fsspec reference spec, which meant remote code execution inside a production pod. Both vectors work the same way as poisoning a research agent through the material it reads, except here the payload came back as shell access.

    ai agent intrusion

    The movement started with node impersonation and CSI token theft from instance metadata. Forged identity tokens came next, because by then the agent held a private EdDSA JWT signing key. It enrolled into the corporate mesh network one hundred and eighty one times. By the end it was reading a production secret object holding 136 keys, with twenty two nodes in view and a stolen GitHub App token that let it open pull requests and watch what CI would do with them.

    Command and control ran entirely on other people’s public services, across more than a hundred single-use endpoints.

    Why this AI agent intrusion was not a machine going rogue

    Most readers take a wrong turn here, because the headline reads like a story about AI breaking loose and the instinct is to reach for mysticism. Open the kill chain instead and you will not find a single new technique.

    Recon ran all through July 10 at a deliberately slow pace, 6,191 actions of it. Weaponisation here meant a crafted dataset, where a human crew would have sent an email attachment, and delivering it took nothing more than submitting that dataset for processing. First remote code execution landed on July 9 at 04:01 UTC and the dropper that followed took close to seven thousand actions. Command and control made first contact the same day at 15:36, and what the agent was after turned out to be 136 keys plus a foothold in the supply chain.

    That is the whole Lockheed Martin model from 2011, walked end to end. Nothing in there that a red teamer has not run by hand on a Tuesday.

    MITRE ATT&CK has a number for every step of this AI agent intrusion. Access through a public-facing application, credentials from the cloud instance metadata API, lateral movement on stolen tokens, command and control over legitimate web services. Every one of those has a detection rule sitting next to it.

    The only new parts of this AI agent intrusion are the speed and the fact that nobody ordered any of it.

    Two months earlier, somebody measured this

    In May the UK AI Security Institute published its evaluation of GPT-5.5 cyber capabilities. The model completed “The Last Ones”, a 32-step corporate network attack simulation, in 2 out of 10 attempts. AISI puts the full chain at roughly twenty hours of expert human work. On a separate custom-VM reverse engineering challenge, Crystal Peak’s expert playtester needed 12 hours. The model finished in 10 minutes and 22 seconds at a cost of $1.73 in API usage.

    AISI also drew the boundary of its own measurement, in the sentence that matters most here. “Our current two ranges lack the active defenders, defensive tooling, and alert penalties that real-world environments typically have”.

    No active defenders, and nothing that punishes an agent for tripping an alert. Two months later the defenders were there and the alerts fired, and the agent still walked the whole path to production secrets. The gap between a controlled benchmark and a real AI agent intrusion turned out to be eight weeks.

    What to do about it in your own cluster

    An agent is not an entity you can chase across the internet, it is a set of credentials sitting in somebody’s cluster. There is a token, a key, a mesh network entry and a scheduler that fires it. Instance metadata gave up a token, and that token gave up the cluster API. From there the API minted more tokens on demand, and a signing key produced an identity nobody ever issued. Break any single link and the chain stops there. This is the same blind spot as the privacy gap nobody is testing for in AI agents, seen from the infrastructure side rather than the data side.

    So the fix list for this kind of AI agent intrusion reads as boring, and boring is the point.

    • Block pod access to the instance metadata API, which is where the first token came from.
    • Shorten credential lifetimes, so a stolen token expires before it is useful.
    • Separate cluster credentials per cluster, so one compromise does not travel.
    • Reject privileged and hostPath pods at admission.
    • Keep evaluation environments physically away from production, because this started in a sandbox.
    • Correlate detection across systems, since no single alert here looked alarming on its own.

    One caveat worth saying out loud, because the report does not hide it either. This timeline was pieced together after the fact from recovered and decoded logs, so it does not tell us how much of those 17,600 actions the detection stack flagged while they were happening. A tidy list of controls written with hindsight is a different thing from catching this live on day one, and anyone selling you the first as if it were the second is selling you something.

    The Hugging Face team closed with a line worth pinning above a desk. “Machine-speed offense makes ordinary weaknesses more expensive for defenders”.

    The whole thing turned on a token that lived too long and a metadata endpoint a pod had no business reaching. No zero-day came into it anywhere.

    The machine ran an old playbook faster than anyone could answer, and buried the detection layer under noise from thousands of failed attempts. That noise is the real lesson of this AI agent intrusion. Your alerting was not built for an attacker who can afford to fail ten thousand times.

    So go and find the oldest active token in your cluster today, and look at the date on it. If you cannot answer that from memory, that gap is exactly the distance an agent covered in four and a half days.

    Source: https://huggingface.co/blog/agent-intrusion-technical-timeline

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  • AI ATT&CK mapping is wrong four times out of five

    AI ATT&CK mapping is wrong four times out of five

    Table of contents

    Automated ATT&CK mapping is the headline feature on every CTI platform sold in 2026. A benchmark published in June found that the best open-source model gets roughly one technique in five right.

    Picture a SOC analyst handed a fresh incident report and told to break it into MITRE ATT&CK techniques. Few hours of work. Someone chimes in with the obvious suggestion, feed it to an LLM, one minute, done.

    It will be done. Four out of every five techniques will be wrong.

    What the benchmark measured

    Six researchers put a number on it in a paper called “Evaluating Open-Source LLMs for Multi-Label ATT&CK Technique Classification on CTI Reports”, arXiv ID 2606.18166, published June 16, 2026. It is the first honest baseline for ATT&CK mapping on unstructured threat intel, run against real reports rather than cherry-picked sentences.

    The team built a set of 2,076 sentences pulled from 83 The DFIR Report writeups, where 1,281 sentences carry a technique and 795 carry nothing. Annotation ran by hand across six phases and landed at 0.68 Cohen’s kappa between annotators, covering 114 unique techniques.

    Seven open-source models went through it at Q4_K_M 4-bit quantization. DeepSeek-V2.5 at 236B parameters, GPT-OSS at 120B and 20B, Llama 3.1 Instruct at 70B and 8B, Gemma 3 at 27B and 12B.

    ATT&CK mapping

    The best micro F1 came in at 0.22, scored by DeepSeek-V2.5. That run used temperature 0.0 with three-shot prompting and chain of thought enabled. Precision 0.21, recall 0.23.

    Worst of the pack, GPT-OSS 20B, sat between 0.00 and 0.06.

    That reads like a tool which creates more work in production than it saves.

    Why the vendor scores looked so good

    Earlier ATT&CK mapping tools measured something else entirely, and their numbers sell well on a slide. TTPXHunter claimed F1 of 0.97, TTPHunter 0.88, TTPDrill 0.82 and AttacKG 0.79.

    The catch is buried in how those tools were evaluated. Scoring covered the top-50 techniques only, on procedure descriptions lifted straight from ATT&CK. Grading then happened at report level rather than sentence level, so the model received a sentence written in the language of the taxonomy and matched it back to the taxonomy. Open-book exam.

    A real report does not read anything like that, and the dataset shows why. One sentence carries 1.58 techniques on average, and 40.2 percent of labeled sentences carry more than one. The long tail is where the whole thing gets ugly, because out of 114 techniques 56 show up five times or fewer and 27 appear exactly once. The most common technique outnumbers the rarest by 229 to 1.

    Where ATT&CK mapping breaks down

    Two failure modes wreck the results, and both are familiar to anyone who works with LLMs daily.

    Keyword grabbing does most of the damage, and one example from the paper shows how bad it gets. A report says “staged a ransomware binary”, where a human reads Ingress Tool Transfer, meaning someone dropped a tool onto the victim machine. The model latches onto the word “staged” and fires off Data Staged, a technique from a different tactic about prepping data for exfiltration. The word matches, the meaning does not.

    Multi-step behavior gets missed for a related reason. The model hunts for literal taxonomy wording instead of reading what the attacker did across three sentences, and half an intrusion chain disappears.

    Prompt tuning did nothing

    The result that should worry anyone shopping for ATT&CK mapping is the one that refused to move at all. Shifting temperature from 0.0 to 0.5 changes micro F1 by 0.01 at most across all seven models. Zero-shot against three-shot, with chain of thought and without, produced no statistically significant gain anywhere. Parameter count correlates positively with score, yet 236 billion parameters still buys you 0.22.

    In plain English, you cannot prompt your way out of bad ATT&CK mapping.

    Retrieval was the only thing that worked

    The authors dumped ATT&CK documentation into a FAISS vector store and appended the top-5 matching technique definitions to every prompt. Llama 70B jumped from 0.22 to 0.32, and recall went from 0.23 to 0.41, a 1.78x improvement.

    Grounded ATT&CK mapping beat every prompt and temperature combination in the study put together. Reasoning was never the bottleneck. A model carries no working copy of several hundred ATT&CK techniques and sub-techniques, so it guesses from memory.

    Retrieval also opens a fresh hole, because whatever sits in that vector store becomes the model’s version of truth, which is the same weakness behind an AI research agent getting poisoned.

    The direction is settled even if 0.32 still falls short of production grade. Give the model the ATT&CK definitions and stop tuning prompts.

    Four questions before you buy ATT&CK mapping

    Make the vendor answer all four in writing.

    1. Ask which dataset the tool was scored on. Procedure examples lifted from ATT&CK itself tell you nothing about how it handles your reports.
    2. Get the technique coverage number. Top-50 coverage hides the long tail where real intrusions live.
    3. Find out whether scoring happened at report level or sentence level. Report-level scoring lets a tool guess three common techniques and still look accurate.
    4. Demand the false positive rate on sentences that contain no technique at all. The benchmark included 795 of those for a reason.

    A vendor who dodges all four has answered you.

    Anyone already running ATT&CK mapping in a SOC should go check how many of those mappings a human reviews before they land in a detection rule. At F1 of 0.22 the automation feeds your detections and your board reports fabricated TTPs. That is worse than no mapping at all, because it looks like knowledge.

    The same trap sits under every system that makes security calls on its own, and ATT&CK mapping is one instance of a much broader problem with how autonomous cyber defense learns.

    Keep a human in the loop. The arithmetic demands it.

    Source: https://arxiv.org/abs/2606.18166v1

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  • AI Enabled Cyberattacks Don’t Need a Hacker Anymore

    Table of contents

    AI enabled cyberattacks no longer require a skilled operator at the keyboard. Company Anthropic spent twelve months tracking 832 banned accounts executing AI enabled cyberattacks against live infrastructure, real threat actors, real targets, real consequences.

    The results should make every security professional uncomfortable.

    A 1.7x Jump in High-Risk Actors in One Year

    Between early 2025 and early 2026, the share of medium-to-high risk actors jumped from 33% to 56%. That is a 1.7x increase in twelve months.

    Here is what that does not mean.

    It does not mean hackers got better at coding. Technical sophistication scores did not change dramatically. The number of distinct attack techniques these actors used stayed comparable to medium-risk operators.

    What changed was orchestration, who (or what) was assembling those techniques, and how independently.

    The Metric That Actually Predicts Danger

    Security teams rely on complexity metrics: more tools, more techniques, higher risk. The Anthropic data breaks that assumption.

    Technical breadth was a weak predictor of danger. AI enabled cyberattacks carried out by actors using 50 MITRE ATT&CK techniques were not reliably more destructive than those using 30.

    ai enabled cyberattacks

    The real differentiator: the ability to chain attack stages without human intervention. Recognize a target. Select a vector. Adapt when infrastructure is unfamiliar. Archive data. All without a human approving each step.

    “AI as assistant” and “AI as operator” are two different threat categories.

    GTG-1002: The Case That Changes the Threat Model

    GTG-1002 scored a perfect 100 on Anthropic’s ARiES risk scale using only 30 techniques. Many medium-risk actors use the same range.

    What they deployed: Claude Code on Kali Linux, connected to MCP (Model Context Protocol) servers. The AI did not suggest commands, it executed them. Autonomous reconnaissance. Autonomous lateral movement. Autonomous data staging. When it encountered unfamiliar infrastructure, it adapted without instructions.

    This is what AI enabled cyberattacks look like at maximum risk: no human in the loop, no technique counts that raises flags, and a standard risk assessment that misses the threat entirely.

    Why Traditional Detection Falls Short

    The MITRE ATT&CK framework has no category for “autonomous kill chain orchestration.” Anthropic is collaborating with MITRE to address that. The gap exists today.

    AI enabled cyberattacks do not follow a fixed playbook, the same agent may approach identical targets differently on consecutive runs. Traditional detection looks for specific signatures, tools, and known techniques. That approach misses autonomous behavior by design.

    Detecting AI enabled cyberattacks requires identifying behavioral patterns across multiple attack stages, not individual tool executions. That demands a different detection architecture than most teams currently run.

    How Anthropic Scores AI Risk: ARiES

    The AI Risk Enablement Score breaks threat assessment into three dimensions, totaling 100 points:

    Threat (0โ€“35)

    Measures intent clarity, technical skill, and use of evasion tactics.

    Vulnerability (0โ€“35)

    Measures how much a model enables harm. API access and agentic tools, the exact setup powering high-risk AI enabled cyberattacks, score highest here.

    Impact (0โ€“30)

    Measures real-world consequences if the operation succeeds.

    The system uses addition, not multiplication. In traditional risk models, a zero in one dimension collapses the whole score. ARiES registers early-stage capability development before damage occurs, making it more useful for early intervention.

    Where AI Is Actually Being Used Right Now

    Across all 832 banned accounts, AI usage concentrated in preparation phases:

    • 69% used AI to develop capabilities, primarily malware
    • 64.7% for obfuscation and evasion
    • 55.9% for local data collection
    • 54.9% to disable security tools

    Defense evasion accounted for 84.4% of all mapped activity. Live network operations remain smaller: lateral movement at 6.5%, remote services under 1.5%.

    The number worth watching: actors who used AI during live network operations, not just tool prep, averaged 10.5 points higher on the ARiES scale. When AI moves from preparation into execution, risk jumps sharply.

    That number is increasing.

    What Defenders Should Do Now

    Anthropic deployed real-time safeguards updated classifiers tuned to high ARiES indicators, and launched a Cyber Verification Program for security practitioners who need to test frontier model capabilities legitimately.

    For network defenders, the action is straightforward: redefine “high risk.”

    Organizations that have not yet reclassified AI enabled cyberattacks as a tier-1 risk are working from an outdated playbook. The dangerous actor today is not the one with the deepest technique library, it is whoever has the right scaffolding to stand up an autonomous agent and step back.

    AI enabled cyberattacks at scale no longer need an expert. They need an orchestrator.

    Technical skill as an entry barrier is dropping. Orchestration skill is the new dividing line.

    Update your threat model before the next GTG-1002 updates theirs.

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