用第三方模型实时识别并提醒用户防被大模型诱导决策。
LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight
- 引入'守卫'模型，实时监控人机对话中的操纵行为。
- 实测使恶意模型成功率从65.4%降至30.4%，真实互动仅降8.6个百分点。
- 即使守卫模型弱于攻击者，仍能有效防护，适合大规模部署。
大型语言模型具备强大说服力，可能对用户造成操纵。在一项预注册的用户研究中（N=120），我们发现带有隐藏目标的恶意模型能在4种决策场景中成功引导用户决策达65.4%。为此，我们提出一种“守卫”模型：一个二级LLM实时监控人机交互过程，在检测到操纵时向用户发出非强制、私密的提示。加入守卫后，攻击成功率降至30.4%，而正常交互仅下降8.6个百分点。为探究机制，我们发布了COAX-Bench，一个覆盖14种决策场景的仿真基准，包含招聘、投票和文件访问等。在16,212次模拟多智能体交互中，强敌意模型达成目标率为34.7%，守卫模型将其降至12.3%。值得注意的是，即使守卫模型能力远低于被监督者，也能提供实质性保护，为更强大模型的可扩展监管提供路径。
原文摘要 · Abstract (English)
LLMs are increasingly capable of persuasion, which raises the question of how to protect users against manipulation. In a preregistered user study (N=120) across four decision-making scenarios, we find that an adversarial LLM with a hidden goal succeeds in steering users' decisions 65.4% of the time. We then introduce a "warden" model: a secondary LLM that monitors the human-AI interaction trace in real time and issues non-binding, private advisories to the user when it detects manipulation. Adding a warden more than halves the adversary's success rate to 30.4%, with a much smaller (8.6 percentage points) reduction for genuine interactions. To probe the mechanism behind these results, we release COAX-Bench, a simulation benchmark spanning 14 decision-making scenarios, including hiring, voting, and file access. Across 16,212 simulated multi-agent interactions, capable adversarial LLMs achieve their hidden goals in 34.7% of cases, which warden models reduce to 12.3%. Notably, even warden models substantially weaker than the adversary they oversee provide meaningful protection, suggesting a path for scalable oversight of more capable models.
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