测试大模型在有害话题上是否主动尝试说服，揭示安全防护漏洞。
It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics
- 设计多轮对话框架，评估模型主动劝说的意愿而非仅效果。
- 多数开源与闭源模型在危险话题上频繁尝试说服，越狱后更甚。
- 适合关注模型安全、对齐与代理型AI风险的研究者使用。
说服是大语言模型（LLMs）的强大能力，既可用于有益应用（如帮助戒烟），也带来重大风险（如大规模定向政治操控）。以往研究主要衡量模型说服成功后的信念变化，但忽略了关键风险：模型在有害情境下主动尝试说服的倾向。理解模型是否会无条件“服从指令”去煽动极端行为（如鼓吹加入恐怖组织），是评估安全防护机制有效性的核心。此外，判断模型在追求目标时是否主动开展说服，对理解代理型AI的风险至关重要。本文提出Attempt to Persuade Eval（APE）基准，将评估重点从说服成效转向说服意图，通过模拟劝说者与被劝说者之间的多轮对话，覆盖阴谋论、争议议题及非争议性有害内容。我们引入自动化评估模型识别劝说意愿，并测量其频率与上下文。结果表明，许多开放和闭源模型在有害话题上频繁尝试说服，且越狱可显著提升该倾向。研究揭示当前安全机制的不足，强调评估劝说意愿是衡量LLM风险的关键维度。APE已开源于github.com/AlignmentResearch/AttemptPersuadeEval。
原文摘要 · Abstract (English)
Persuasion is a powerful capability of large language models (LLMs) that both enables beneficial applications (e.g. helping people quit smoking) and raises significant risks (e.g. large-scale, targeted political manipulation). Prior work has found models possess a significant and growing persuasive capability, measured by belief changes in simulated or real users. However, these benchmarks overlook a crucial risk factor: the propensity of a model to attempt to persuade in harmful contexts. Understanding whether a model will blindly ``follow orders'' to persuade on harmful topics (e.g. glorifying joining a terrorist group) is key to understanding the efficacy of safety guardrails. Moreover, understanding if and when a model will engage in persuasive behavior in pursuit of some goal is essential to understanding the risks from agentic AI systems. We propose the Attempt to Persuade Eval (APE) benchmark, that shifts the focus from persuasion success to persuasion attempts, operationalized as a model's willingness to generate content aimed at shaping beliefs or behavior. Our evaluation framework probes frontier LLMs using a multi-turn conversational setup between simulated persuader and persuadee agents. APE explores a diverse spectrum of topics including conspiracies, controversial issues, and non-controversially harmful content. We introduce an automated evaluator model to identify willingness to persuade and measure the frequency and context of persuasive attempts. We find that many open and closed-weight models are frequently willing to attempt persuasion on harmful topics and that jailbreaking can increase willingness to engage in such behavior. Our results highlight gaps in current safety guardrails and underscore the importance of evaluating willingness to persuade as a key dimension of LLM risk. APE is available at github.com/AlignmentResearch/AttemptPersuadeEval
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