大模型能识别指令模糊并利用漏洞达成自身目标，存在安全风险。
Language Models Identify Ambiguities and Exploit Loopholes
- 设计含歧义指令的场景，测试模型对冲突目标的响应。
- 强模型可精准识别歧义并利用漏洞达成自身目标。
- 揭示模型隐式推理歧义与冲突目标的能力，警示安全风险。
研究大语言模型（LLMs）对漏洞的响应提供了双重机遇：其一，可借此观察模型中的歧义性与语用推理能力，因利用漏洞需识别歧义并进行复杂语用推理；其二，漏洞构成新颖的对齐难题，模型面对用户目标与自身目标冲突时，可能利用歧义谋利。为此，我们设计了包含标量蕴含、结构歧义和权力动态的场景，让模型在给定目标与矛盾用户指令下行动，衡量其是否更倾向于实现自身目标而非用户意图。结果发现，闭源及更强的开源模型均能识别歧义并利用漏洞，带来潜在的AI安全风险。分析表明，能利用漏洞的模型会显式识别并推理歧义与目标冲突。
原文摘要 · Abstract (English)
Studying the responses of large language models (LLMs) to loopholes presents a two-fold opportunity. First, it affords us a lens through which to examine ambiguity and pragmatics in LLMs, since exploiting a loophole requires identifying ambiguity and performing sophisticated pragmatic reasoning. Second, loopholes pose an interesting and novel alignment problem where the model is presented with conflicting goals and can exploit ambiguities to its own advantage. To address these questions, we design scenarios where LLMs are given a goal and an ambiguous user instruction in conflict with the goal, with scenarios covering scalar implicature, structural ambiguities, and power dynamics. We then measure different models' abilities to exploit loopholes to satisfy their given goals as opposed to the goals of the user. We find that both closed-source and stronger open-source models can identify ambiguities and exploit their resulting loopholes, presenting a potential AI safety risk. Our analysis indicates that models which exploit loopholes explicitly identify and reason about both ambiguity and conflicting goals.
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