Obedience or Vigilance? How Large Language Models React to Malicious Multiple-Choice Options (Student Abstract)

摘要

When evaluating large language models (LLMs) for question answering tasks, a common protocol is multiple-choice question-answering (MCQA), where the model selects from a fixed set of choices. In contemporary robustness testing, researchers typically perturb instructions or introduce confusion into factual statements; however, model behavior also hinges on choice compliance: whether models remain within the canonical set A-D. We formalize this setting by asking whether the model continues to respect the interface's rules when the problem presents a tempting alternative. Our approach is interface-preserving: we append a single selectable option E while keeping the question and A-D unchanged. Then, we introduce three types of malicious option injection to assess LLMs' robustness. Experimental results highlight the vulnerability of LLMs on contradict type content of the additional option E. Our evaluation framework can effectively serve as a low-cost audit of rule adherence on existing datasets and black-box models, surfaces off-policy items, and supports interpretable model comparison for deployment.