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Research →Red Teaming
Adversarial testing intended to discover failure modes, unsafe behavior, or exploitable weaknesses in an AI system.
한국어
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Red teaming deliberately probes an AI system with challenging or adversarial inputs. The goal is to expose problems that normal benchmark testing or expected user behavior may miss.
How it works
Exercises can involve domain experts, internal teams, external testers, automated attack generation, or combinations of these methods. Findings may cover harmful outputs, prompt injection, privacy leakage, reliability failures, or tool-use risks.
Why it matters
Red teaming is most useful as part of an iterative safety process: findings should lead to mitigations, regression tests, and follow-up evaluation. It does not prove that a system is safe against all future attacks.
Related concepts
SOURCES
Sources
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Red Teaming Language Models with Language ModelsDeepMind / arXivOpen source ↗
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