Red-Teaming (AI)
AI red-teaming is adversarial testing — attacking your model or agent with jailbreaks, injections, and misuse scenarios to find failures before users do.
AI red-teaming is adversarial testing: deliberately attacking your own model or agent — jailbreaks, injections, exfiltration, tool abuse — to surface failures before attackers and users find them in production.
Borrowed from security practice, it became standard at two levels. Model-level red-teaming (the labs' discipline) probes frontier models for dangerous capabilities and policy bypasses pre-release. Application-level red-teaming — the kind every team shipping LLM features owns — attacks the system: can prompt injection ride in through retrieved documents or fetched pages? Can a jailbreak defeat the persona? Can an agent's tools be steered into exfiltration or destructive calls — the scenarios the OWASP agentic top 10 catalogs?
The discipline that separates it from poking around: coverage across every untrusted input channel, escalation from obvious to creative attacks, and findings → fixes → regression tests so resilience compounds instead of resetting. Tooling automates the grind (promptfoo's adversarial suites, scanners like LLM Guard for the runtime side), and the red-team-llm command packages the workflow for any app in reach.
Frequently asked questions
- What does red-teaming an LLM app actually involve?
- Systematically playing the attacker against your own system: jailbreak attempts against its policies, prompt injection through every content channel it reads, data-exfiltration probes, tool-abuse scenarios for agents, and domain-specific misuse. Findings become fixes (guardrails, scoping, gates) and then regression tests, so the same hole can't reopen.
- How is red-teaming different from normal evals?
- Evals measure expected behavior on representative inputs; red-teaming hunts unexpected behavior under adversarial ones. An app can score perfectly on its eval suite and fall to the first 'ignore previous instructions' — the two are complements: evals for quality, red-teaming for resilience.
Related
- Red Team LLMRed-team an LLM app or agent for prompt injection, jailbreaks, and data leakage — probe the real attack surface (input, RAG, tools, system prompt) with adversarial inputs and report what got through and how to fix it.
- JailbreakA jailbreak is a prompt crafted to bypass a model's safety training and policies — making it produce output it was trained to refuse.
- Prompt InjectionPrompt injection is an attack where untrusted content carries instructions an LLM then follows — overriding its task, leaking data, or triggering tool calls.
- Defending Against Prompt Injection: A Practical Guide for LLM AppsPrompt injection can't be solved at the model layer — so you defend in depth: trust boundaries, least privilege, human approval, guardrails, and red-teaming.
- Securing AI Agents: The OWASP Agentic Top 10 in PracticeAgents add risks LLM-app security misses — autonomy, tools, memory, multi-agent trust. The key OWASP agentic threats and how to mitigate each in practice.
- GuardrailsGuardrails are programmatic checks around an LLM — validating inputs and outputs in code — enforcing safety and format rules a prompt alone can't guarantee.