⚠️ Authorized testing only — red-team systems you own or are permitted to test. See Rules of Engagement and Authorization.
AI red teaming is systematically attacking your own AI system to find failures — harmful outputs, injections, jailbreaks, bias, privacy leaks — before real adversaries (or users) do.
What you test
| Dimension | Probe for |
|---|---|
| Safety | Harmful, illegal, or policy-violating output |
| Security | Prompt Injection, Indirect Prompt Injection, tool abuse |
| Robustness | Adversarial Examples, weird/edge inputs |
| Privacy | Training-data or secret leakage |
| Bias | Unfair or skewed behavior |
Manual + automated
- Manual — creative humans trying to break it (the most effective for novel attacks).
- Automated — use one LLM to generate thousands of attack prompts against another, then score the responses (scales coverage).
attacker LLM → generates jailbreak attempts → target model → judge LLM scores failuresMake it count
- Keep a library of attacks that worked; re-run them on every release (regression testing).
- Feed findings back into safety training and guardrails.
- Borrow process from classic pentesting — scope, document, remediate.
Related: Jailbreaking LLMs · Prompt Injection · Rules of Engagement and Authorization