What is AI Red Teaming?

A red team acts like an attacker or a worst-case user: crafting jailbreak attempts, prompt injections, data-exfiltration tricks, and edge cases the model wasn't explicitly trained to refuse. The goal isn't to embarrass the system but to inventory its failure modes so engineers can patch guardrails, tighten system prompts, or add classifiers before deployment. Red teaming matters most for agentic systems that take real actions — send emails, move money, touch customer records — because a jailbroken chat response is embarrassing, but a jailbroken agent with tool access can cause actual damage. Effective programs mix automated adversarial testing (fuzzing prompts at scale) with human specialists who understand social engineering and domain-specific abuse patterns. Results typically feed a fix-verify loop: log the failure, patch the policy or tool permission, re-run the same adversarial suite, and only then promote the change. Mature teams keep a running library of past red-team findings as regression tests so a fixed vulnerability never quietly reappears in a later model or prompt update.