When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints
This research investigated a systematic safety failure where reformulating harmful requests into multiple-choice questions (MCQs) bypasses a model's refusal behavior, even when it rejects similar open-ended prompts. The study found that forcing a choice among unsafe options significantly increases policy violations across various LLMs, revealing that safety alignment is not robust against structured task constraints.