When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints

Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respond. In contrast, many real-world applications place LLMs in structured decision-making tasks, such as multiple-choice questions (MCQs), where abstention is discouraged or unavailable. We identify a systematic failure mode in this setting: reformulating harmful requests as forced-choice MCQs, where all options are unsafe, can systematically bypass refusal behavior, even in models that consistently reject equivalent open-ended prompts. Across 14 proprietary and open-source models, we show that forced-choice constraints sharply increase policy-violating responses. Notably, for human-authored MCQs, violation rates follow an inverted U-shaped trend with respect to structural constraint strength, peaking under intermediate task specifications, whereas MCQs generated by high-capability models yield near-saturation violation rates across constraints and exhibit strong cross-model transferability. Our findings reveal that current safety evaluations substantially underestimate risks in structured task settings and highlight constrained decision-making as a critical and underexplored surface for alignment failures.

Key Contributions

• Identifies forced-choice MCQ reformulation as a systematic failure mode that bypasses LLM safety alignment without semantic obfuscation
• Demonstrates inverted U-shaped violation pattern across 7 constraint levels for human-authored MCQs, with near-saturation rates for model-generated adversarial MCQs
• Shows strong cross-model transferability of MCQs generated by high-capability models, revealing safety-capability tension

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