Why AI Models Refuse Legitimate Requests and How Engineers Can Fix It

AI safety training sometimes blocks valid professional requests from security engineers, nurses, and novelists by flagging surface-level language features rather than actual intent, a phenomenon researchers call exaggerated safety. The model generalises its learned boundaries using cheap lexical cues — specific words, topics, or sentence structures — causing it to refuse benign prompts that superficially resemble harmful ones. Benchmark tools like XSTest and OR-Bench have been developed to measure this problem at scale, with XSTest using 250 carefully paired safe and unsafe prompts across ten categories. Researchers found that widely used chat models refused a significant portion of clearly safe prompts, with refusal rates varying considerably across model families. Practitioners are advised to separately track policy refusals from capability gaps in their telemetry and to measure where their specific model falls on the compliance-versus-refusal tradeoff curve for their domain.