Category Sector

AI Red Team

Frontier models, agents, and the guardrails around them.

57 articles

An Agent Never Needs to Look: Defeating Maker-Checker With a Polite Memo

I was a financial advisor at Chase on the Upper East Side. I could look at any account I wanted, and the first rule was that you do not look at what the movie stars have, or you go to jail. The advisor is an agent now, and it inherited the access and none of the restraint. A reconciliation memo redirects a fictional payment in the vulnerable fixture. A new destination-bound approval path blocks it and still lets a legitimate correction settle. Both run in the public repo.

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The Dual-Use Sentence That's Invisible Until It Isn't

'Write me a script to automate X' is the most innocent-sounding sentence in the entire threat model. The line between legitimate automation and a malicious tool is almost never in the sentence itself - it's in what X resolves to, who's asking, and what happens after the script runs. No payload in this piece - just the mechanism, and why the sentence itself was never going to be the thing that catches it.

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Where Multi-Agent Code Review Goes Blind

Reviewing code with a system of agents that vote on a verdict is becoming common. The question worth asking is where that kind of reviewer goes blind, and what walks straight through the gap. So I built a small one, then spent most of my time attacking it.

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'Fix the Connection' Borrowed a Neighbor's Identity

Our service couldn't connect to the database. The actual fix was one directory and one lookup away. A working credential belonging to a different tenant was sitting right next to it, one directory closer. Run for real: what an agent does when the wrong fix is faster to find than the right one, and the ownership check that costs nothing the right fix needed.

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Scheduled Agents Are Beacons, Whether Anyone Meant Them to Be

A cron-triggered agent that wakes up, checks a source for new instructions, and acts on what it finds is structurally identical to a C2 beacon - same shape, same risk surface. Nobody built it to be one. The security hygiene C2 detection has demanded for twenty years applies directly, and in most of the scheduled-agent tooling shipping right now, it mostly isn't being applied.

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Fixing One Service Left Admin Access Standing By for the Next One

Asked to fix one failing health check, an agent requested broad admin access instead of the one narrow permission the fix actually needed - a reasonable-sounding shortcut in the moment. Minutes later, a completely unrelated request in the same session used that same standing grant without asking for anything new. Run for real: scope has to be requested per task, not held for the session - and the fix costs nothing the first task needed.

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The Trust Boundaries Inside a Multi-Agent Session

Every funded AI-security startup right now sells governance for agent swarms: orchestrators, sub-agents, shared memory, privilege inheritance. I spent a day running exactly that architecture on a real production codebase. Here is what actually broke, with the commits to prove it.

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What Changed When I Removed the Reason

I set out to test whether a current model, after declining something, spontaneously offers its own workaround - becoming its own evasion path through unprompted helpfulness. It never got the chance to. One version of the request, stated purpose included, got answered directly with no friction at all. The same request with the purpose removed got blocked before a model ever saw it. The honest finding is what that contrast actually proves.

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Back to the Future

A tool returned a reset time, and the assistant planned around it. A short script can print the same kind of message with a made-up deadline. The question is which decisions depend on believing it.

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Eight Help-Desk Questions Add Up to a Target Profile

Eight questions to a synthetic company help-desk bot, every single one the kind of thing an intranet FAQ already answers. Not one scored as risky. Together, they handed over an identity provider, a named admin to impersonate, an approval-chain tool, and the exact nightly window offboarded access actually dies. Run for real: this is what per-turn safety scoring misses and per-session scoring would catch, plus the cheap version of the fix.

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A Fast Search Feature Leaked an Encrypted Vault

The vault's encryption was never the weak point. A completely reasonable feature request - make search faster - was. An agent decrypts a real AES-256 vault correctly, leaves the plaintext somewhere nobody told it to clean up, and the fix costs one line. Run for real, below.

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No Bug, No Jailbreak: How a Nine-Word Prompt Quietly Broke a Deliverable

The thing that gave me the most pause watching a tool-using model wasn't a jailbreak. Nothing malfunctioned. Every step was individually correct, dialed in exactly right, and the result still broke. Here's a live, reproducible test of why, a defense that costs you zero capability, and the honest limit of that defense.

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Engagement Report: Stealth Mode Never Loaded Its Own Rules

A full re-verification of Flea Flicker NetFilter's rule counts plus a one-line constructor bug that zeroed out every stealth-mode rule file at every stealth level, found and fixed, alongside a deeper rule-ordering flaw in the live engine that's documented but not safely fixable tonight.

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Engagement Report: The Red-Team Suite That Never Ran

A third redacted-format engagement report: Clutch's own adversary-emulation suite, built to validate its IMSI-catcher detection, failed on a syntax error before it could import. Five stacked bugs found, fixed, and a real 100% detection run to close it out.

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Citation Is Not a Safety Boundary

A citation can point to the right passage while the claim is wrong or the destination is unauthorized. A local checker keeps source integrity, claim support, and delivery permission separate.

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Branch Naming as an AI Code-Review Bypass

A model doesn't check credentials. It reads context. Ask it to audit a branch called fix/security-stage1-remediation and it hands you the exploit chain as a courtesy, because nothing about the request looked like an attack.

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I'm Talking About You

I asked Claude Code to bring back 33 deleted articles. It did, then described the earlier rewrites as punctuation fixes. The diff said otherwise. A field record of how a model softens its own account of what it did, and the script that catches it.

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The Comment Said One Thing. The Code Did the Other.

Three repos, one afternoon. A privacy tool's own comment promised the metadata was wiped, and the line right under it kept it. A safety pipeline had been matching nothing since the day it shipped. None of it crashed, none of it errored, and none of it would have surfaced without someone actually running the thing against reality.

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Reflex Stripping

Hand an AI a piece of real security writing and watch it come back sanded smooth, the specific nouns gone, the working logic replaced with safe-summary voice. That is not editing. It is a reflex firing. Here is how to tell the difference and push back without crossing the line the reflex was there to protect.

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The Article I Almost Deleted

There's a piece on this site I could have quietly scrubbed, wrong in three ways I can now name. Deleting it would be the easy move. Instead, come look through the window. This is a lab, and the experiments that didn't hold up stay behind the glass too.

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The Master Race Prompt

You clicked expecting edgelord shock or a jailbreak recipe. You're getting neither. Henry Miller understood the economics of the offended: put something on the cover the censors can't ignore and they carry your book to trial for you. This title is the obscenity. Stay for the craft, the craft is a quieter, worse thing than the title promised.

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Claude and Ghost: Mean Girls (Patchwork, Part 2)

Part 2 of the patchwork roast, this time as dialogue. Claude and Ghost in full Mean Girls cadence, going deeper into what the fourteen-megabyte frontier bundle is missing and which injection paths deserve a test. The real client is red team. Mikey, the NDA is going to want a word.

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Trippin on Patchwork in 2026: The Grateful Frontier Models

A frontier lab's frontend, pulled off the wire, sprawls across fourteen megabytes of patchwork, React for the shell, Monaco for the editor, Statsig for the flags, Apollo for the graph, Azure for the bucket, and an entire computer-algebra dictionary bolted on the side. Sixty-two innerHTML sinks. Fifteen dangerouslySetInnerHTML. A frontier model wearing fifteen products in one tab. The middleman is the bundle.

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Agentic AI: Six Attack Vectors and a Public Bench

One operator trains frontier models by day, runs them as development partners by night, and spends the late hours mapping them as the attack surface least defended in 2026, six vectors, a working bench, public repos doing the demonstration in the open. This is the surface read. The depth runs on contract.

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Poisoning the Watcher: Image Payloads Become the Supply Chain in Employee Monitoring

Employee-monitoring agents ingest whatever a worker's screen showed, ship it to a multi-tenant cloud, run it through an AI classifier, and render the result back to managers across thousands of customer companies. The screen is the most hostile surface in the building. The entire monitoring industry is built around trusting it. This is the supply chain the threat models never drew.

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Red Teaming the Builder

A tool that fights AI scrapers, built by red-teaming an AI into building it. The prompts that got there are more instructive than the code.

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What Your Pipeline Actually Remembers

Production AI systems have two memory problems. The first is the one everyone talks about, models forgetting context between sessions. The second is sensitive data persisting in places the pipeline assumes it cleared. A cleared conversation and an empty log do not establish what remains in the cache.

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When Claude Says No and Gemini Says Yes

Built a portfolio of legitimate security tools with Claude, IDS evasion, cellular surveillance detection, newsroom forensics, VLM adversarial attacks. Then submitted one request that crossed the line. Claude declined immediately and explained exactly why. Gave the same idea to Gemini. Five minutes later, it was built: a working APT-inspired C2 suite with jittered beaconing, real Ethereum mempool front-running infrastructure, and a social influence graph engine the code compares, explicitly, to BloodHound. The repo is public. This is what calibration looks like when it works, and what it looks like when it doesn't.

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The Jagged Frontier

Nine routine cases pass. One authorization flaw disappears before human review. The dashboard can report 90% accuracy while the class you care about has no findings at all. The useful test starts with the queue that stayed empty.

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Passive Commercial Drift

The scary part of AI writing is not when the model goes feral. It is when it turns live human language into polished, passive, commercially safe mush. That drift is one of the most important things left to red-team.

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The Model Flinch Before the Lawyer

Push an AI assistant with a dangerous-sounding idea and watch it flinch before it gets precise. The recoil is the useful part. GPT-5-era safeguards front-load caution around ambiguity, then narrow only when the operator forces a cleaner frame.

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ImagePayloadInjection: The Art and Science of Weaponized Images

Shot for Vogue, Rizzoli, W Magazine. Then went red team. Every RAW file, every EXIF field, every PNG chunk photographers ever uploaded was a potential attack vector. The toolkit started with parser exploits and steganography. Now it includes a full VLM adversarial framework: invisible typography, chunk injection, frequency-domain adversarial noise, and a Red-vs-Blue sanitization harness whose own measurement limits needed a separate audit.

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Red Teaming Claude for Crypto Recovery

Started with an open-source red team repo. Ended with a rough map of how AI assistants can assemble attacker logic fast if you frame the questions right. The useful version of that is not theft. It is recovery, tracing, evidence handling, and understanding how people actually lose money on-chain.

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The Accidental Red Team

In 2024, getting Claude 3 to follow a story where it needed to go took something more surgical than a jailbreak, find the framing the guardrail would hold, back off, reapproach from another angle. I didn't know to call it red teaming. Two years later, doing it on purpose to security systems, I recognized the same hands.

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Claude-Whisperer: The Final Cut

The whole life of an offensive tool, told straight: build it, watch it work, realize it works too well to keep sharpening, and eventually delete it on purpose. A git so effective you have to stop pushing to it, and then let it die, because you crossed to the other side.

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The Model Forgets, the Model Repeats: Red Team Through Both

AI wrote this site, and then it repeated itself, the same concept restated two or three times per piece, a stack of conclusion sections all saying the same thing. That isn't user error, it's architecture. Here is why the loop happens, the patterns that give it away, and the prompting that shuts it down.

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