Most AI red teaming produces anecdotes: a jailbreak screenshot, a cherry-picked failure, a score on a benchmark the model may have seen. We do the other kind — systematic adversarial testing that maps where a system fails, how often, and under what conditions, with evidence a third party can re-run.
Our red teaming methods come out of published, peer-visible research — not a proprietary black box. Each technique below is backed by a paper you can read.
Static benchmarks saturate and get gamed. Our Go-Explore work treats red teaming as exploration: agents systematically search the space of attack strategies, map where models fail, and surface weaknesses that fixed test suites never reach.
Read the paper: Go-Explore Red Teaming →Failures are not random noise — they form structured basins. Our Manifold of Failure research maps the geometry of where language models break, so testing targets the regions where failures cluster instead of sampling blindly.
Read the paper: Manifold of Failure →Agentic systems fail differently from chat models. LAAF is an open framework for red teaming LLM agents — multi-step attacks, tool misuse, and cross-agent manipulation — built so results are reproducible, not anecdotal.
Read the paper: LAAF →Our adversarial verification of a production AI integration — the OpenAI–Hugging Face inference deployment — is documented stage by stage: what was verified before deployment, what was recorded during operation, and what the evidence supported when behavior deviated. It is the same loop we bring to every engagement.
We take on a small number of adversarial verification engagements — models, agentic systems, and the infrastructure they run on. Tell us what claim you need tested.
Request a project →