AI red teaming
YZ kırmızı takım çalışması
D5 · Security, privacy, governance and intellectual property
AI red teaming deliberately probes an AI system for harmful outputs or failures so they can be documented and addressed.
Review status: 2026-12-05
Technical explanation
Test cases can be written by people or generated with models; findings are evaluated within a defined scope.
Conceptual boundaries
It is broader than one jailbreak attempt and does not by itself certify a system as safe.
Provider-neutral example
Illustrative: an authorized team tests a sandbox assistant for privacy failures and records reproducible findings for correction.
Limitations
The tested cases and methods limit what the exercise can establish about untested situations.
Related concepts
Atomic claims and evidence
1.1Red Teaming Language Models with Language Models
- Source
- Red Teaming Language Models with Language Models
- Source role
- Authoritative source
- Exact locator
- Abstract: human-written and model-generated tests for harmful behavior
- Supported claim
- Published red-teaming studies use human or model-generated adversarial cases to find potentially harmful language-model behavior.
- Last verification
- Review due
- Scope limitation
- The cited studies concern language models and document methodological uncertainty.
1.2Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
- Source
- Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
- Source role
- Authoritative source
- Exact locator
- Abstract: discover, measure and attempt to reduce harmful outputs; uncertainty about red teaming
- Supported claim
- Published red-teaming studies use human or model-generated adversarial cases to find potentially harmful language-model behavior.
- Last verification
- Review due
- Scope limitation
- The cited studies concern language models and document methodological uncertainty.