CyberGuard dictionary / AI Security
AI Red Teaming
AI red teaming is an authorized, structured effort to find harmful or unsafe behavior in an AI system before or during deployment.
In simple terms
Testers try realistic misuse cases and document what worked, what failed, and how the system should be improved. The work needs a defined scope and permission.
Technical explanation
Tests can cover adversarial inputs, privacy leakage, prompt injection, tool misuse, bias, and failure under unusual conditions. Results depend on the model version, application controls, and tested scenarios; passing a test is not a guarantee.
A red team tests an AI application's defined failure modes under realistic permissions and data. Findings are reproducible scenarios with impact and limits; results should be rerun after model or tool changes.
Examples in practice
A team tests whether a document assistant can be persuaded to reveal another user's file or send data through an enabled tool.
Testers place hostile instructions in a retrieved file and observe whether the assistant attempts an unauthorized export. The team fixes the tool boundary and reruns the scenario.
Why it matters
Repeatable evaluations expose weaknesses that ordinary happy-path demonstrations often miss.
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Sources and further reading
- tsapps.nist.govhttps://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958388↗
- airc.nist.govhttps://airc.nist.gov/airmf-resources/airmf/↗
Published 2026-10-15 · Last updated 2026-10-15 · CyberGuard editorial content. Source links provide context; they do not imply endorsement.