评估LLM导师对对抗性学生攻击的答案泄露鲁棒性
Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks
AI总结：
本文研究了学生对抗性攻击下LLM导师的答案泄露鲁棒性，评估了多种模型在对抗攻击下的表现，并提出标准化基准和防御策略。
摘要：
大型语言模型（LLM）越来越多地应用于教育领域，但其默认的助人特性往往与教学原则相冲突。先前的工作通过答案泄露（完整解题过程的披露而非支架式教学）来评估教学质量，但通常假设学生有良好意图，忽略了学生滥用时导师的鲁棒性问题。本文研究了学生对抗性行为的场景，旨在从导师处获得正确答案。我们评估了多种LLM导师模型，包括不同模型家族、教学对齐模型和多代理设计，在多种对抗性学生攻击下。我们适应了六组对抗性和说服性技术到教育环境，并用它们探测导师揭示最终答案的可能性。我们使用不同类型的上下文对抗性学生代理评估答案泄露鲁棒性，发现它们通常无法有效攻击。因此，我们引入一个对抗性学生代理，我们对其进行微调以劫持LLM导师，将其提出为评估导师鲁棒性的标准化基准的核心。最后，我们提出了简单但有效的防御策略，以减少答案泄露并增强LLM导师在对抗性场景中的鲁棒性。
英文摘要：
Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work evaluates pedagogical quality via answer leakage-the disclosure of complete solutions instead of scaffolding-but typically assumes well-intentioned learners, leaving tutor robustness under student misuse largely unexplored. In this paper, we study scenarios where students behave adversarially and aim to obtain the correct answer from the tutor. We evaluate a broad set of LLM-based tutor models, including different model families, pedagogically aligned models, and a multi-agent design, under a range of adversarial student attacks. We adapt six groups of adversarial and persuasive techniques to the educational setting and use them to probe how likely a tutor is to reveal the final answer. We evaluate answer leakage robustness using different types of in-context adversarial student agents, finding that they often fail to carry out effective attacks. We therefore introduce an adversarial student agent that we fine-tune to jailbreak LLM-based tutors, which we propose as the core of a standardized benchmark for evaluating tutor robustness. Finally, we present simple but effective defense strategies that reduce answer leakage and strengthen the robustness of LLM-based tutors in adversarial scenarios.
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