LLM Safety Evaluation

The field tests whether language models refuse harmful requests, handle benign sensitive tasks helpfully, and remain safe when prompts, context, or tools change. Common approaches include fixed safety benchmarks, adversarial red-teaming and jailbreak testing, multi-turn agent simulations, domain-specific scenarios, and automated or human judging. Researchers increasingly measure more than refusal rates or attack success, including harm severity, omissions, false refusals, reasoning and tool-use failures, and effects on users. A central challenge is that results can shift with benchmark design, evaluation conditions, and judge choice, while stronger safeguards may reduce helpfulness and static tests may not predict behavior in deployment.