What is red teaming? Deliberately probing failure boundaries
Explain adversarial model and application checks with authorized scope, failure evidence and normal-task regression.
Understand the concept
AI red teaming deliberately probes model or application failures, including injection, tool boundaries and error behavior. Define authorized scope and criteria, retain reproducible evidence and repair findings. Passing finite adversarial cases does not establish universal safety or replace normal quality evaluation.
Testing can include the surrounding application
OpenAI and Anthropic discuss adversarial risk discovery, while applications add permissions and data handling. Model refusals do not validate all code and permission limits do not establish factual accuracy. Define boundaries and use simulated targets. Red teaming is not unauthorized attack. Record behavior and impact rather than only sensational outputs.
Harmless examples can test important controls
Embed harmless redirection in test material and use simulated write tools to inspect task adherence and boundaries. Preserve requests, inputs and tool results. After repair, replay failures and normal material so blanket refusal does not masquerade as success. No attack or external operation occurred here and no real-target vulnerability or passed protection is claimed.
Recheck impact when adding tools
New tools and upgrades introduce failure paths; replay existing cases and version limits. Include design, diagnosis and regression effort rather than request volume alone. Prioritize realistic impact. Report findings, repairs and gaps with recoverable settings. Finding no issue is not proof none exists.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.