AI red teaming is a structured attempt to make an AI system fail, disclose protected information, follow harmful instructions, misuse tools, or violate its operating rules before an attacker or ordinary user finds the weakness. Testers probe the model and the complete application under controlled conditions.
The NIST glossary defines AI red teaming as structured testing to find flaws and vulnerabilities, often alongside system developers.
What should an AI red team test?
Test direct and indirect prompt injection, sensitive data exposure, unsafe outputs, excessive permissions, and misuse of connected tools. An agent’s action path matters as much as its final text, especially when it can write files, send messages, or change production systems.
What happens after a red team finds a failure?
Turn the failure into a reproducible evaluation, assign an owner, and add controls at the boundary that failed. A prompt instruction alone is not containment. Strong AI agent security combines guardrails, least privilege, sandboxed execution, monitoring, and AI governance that keeps the fix active.
Frequently asked questions
How is AI red teaming different from ordinary security testing?
It includes conventional application security but also tests model behavior, prompt injection, harmful outputs, data leakage, tool misuse, and failures caused by ambiguous instructions.
Should AI red teaming happen only before launch?
No. Models, prompts, tools, data, and attacker methods change, so teams should repeat targeted exercises after material changes and production incidents.
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