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.
View on arXiv@article{liu2025_2504.10430,
title={ LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models },
author={ Minqian Liu and Zhiyang Xu and Xinyi Zhang and Heajun An and Sarvech Qadir and Qi Zhang and Pamela J. Wisniewski and Jin-Hee Cho and Sang Won Lee and Ruoxi Jia and Lifu Huang },
journal={arXiv preprint arXiv:2504.10430},
year={ 2025 }
}