Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety risks of LLM-driven persuasion, particularly their potential for unethical influence through manipulation, deception, exploitation of vulnerabilities, and many other harmful tactics. In this work, we present a systematic investigation of LLM persuasion safety through two critical aspects: (1) whether LLMs appropriately reject unethical persuasion tasks and avoid unethical strategies during execution, including cases where the initial persuasion goal appears ethically neutral, and (2) how influencing factors like personality traits and external pressures affect their behavior. To this end, we introduce PersuSafety, the first comprehensive framework for the assessment of persuasion safety which consists of three stages, i.e., persuasion scene creation, persuasive conversation simulation, and persuasion safety assessment. PersuSafety covers 6 diverse unethical persuasion topics and 15 common unethical strategies. Through extensive experiments across 8 widely used LLMs, we observe significant safety concerns in most LLMs, including failing to identify harmful persuasion tasks and leveraging various unethical persuasion strategies. Our study calls for more attention to improve safety alignment in progressive and goal-driven conversations such as persuasion.

本研究主要解决了大型语言模型（LLMs）在说服中的潜在伦理风险，特别是它们可能通过操控、欺诈及利用脆弱性来进行不道德影响的问题。我们提出了一个名为PersuSafety的框架，以系统评估LLMs的说服安全性，并通过大量实验发现大多数LLMs在识别有害说服任务和使用不道德策略方面存在显著安全隐患。此研究强调改善在说服等渐进性和目标驱动对话中的安全对齐的重要性。

大型语言模型可能是危险的说服者：关于说服安全性的实证研究