AI Security & Red TeamingThe attackers have frontier models. So do we.

AI red teaming is adversarial testing of the whole AI system: model behavior, prompts, tools, retrieval, permissions, memory, logs, APIs and the application around them. We try jailbreaks, prompt injection, poisoned documents, unsafe tool calls, data exfiltration and multi-step abuse, then reproduce each finding, rank its severity, fix the architecture and turn the failure into a regression test.

Distillation trains one model on another model’s outputs. Run at scale against a frontier model’s API, it copies much of that model’s skill without its safeguards. Once the refusals are stripped out, the result is an “abliterated” model that will do offensive security work the original would refuse, for anyone who downloads it.

No, and that is by design. The labs give their strongest offensive models only to vetted partners. We run them against your systems under agreed rules of engagement, and you get reproduced findings, severity rankings, fixes and regression tests.

No. A jailbreak attacks the model directly, usually through the user’s prompt. Prompt injection hides instructions inside content the system reads: documents, web pages, emails, tickets and tool outputs. In RAG and agent systems that content crosses a trust boundary quietly, so defenses have to keep user intent, system rules, retrieved facts and tool output apart.

No. Guardrails catch obvious bad outputs. They can’t fix an architecture that lets a model read secrets, change production systems or call dangerous tools without oversight. Safe systems layer least-privilege tool access, data boundaries, retrieval controls, monitoring, human approval for sensitive actions and incident response, so one failed control doesn’t become a breach.