title: LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety
    in Large Language Models
summary: 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.
paper_authors._id: 67fe093302ae092e4306af12
paper_authors.name: Minqian Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:32:23.136Z
paper_authors._id: 64c32a75d15a8812b71afc48
paper_authors.avatarUrl: /avatars/4186c592b00aea73fb8c5bb719935ce7.svg
paper_authors.fullname: Minqian Liu
paper_authors.type: user
paper_authors.user: mqliu
paper_authors._id: 67fe093302ae092e4306af13
paper_authors.name: Zhiyang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:32:36.033Z
paper_authors._id: 64b6c686cf5117d7962d8f62
paper_authors.avatarUrl: /avatars/96ed7a9602aa4c21b3a3d89608e76dc8.svg
paper_authors.fullname: Zhiyang Xu
paper_authors.type: user
paper_authors.user: Zhiyang03
paper_authors._id: 67fe093302ae092e4306af14
paper_authors.name: Xinyi Zhang
paper_authors._id: 67fe093302ae092e4306af15
paper_authors.name: Heajun An
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:32:49.203Z
paper_authors._id: 679d30bf48f48796199c415e
paper_authors.avatarUrl: /avatars/c28e0dd7c9f6f62bccdd8eb2c8772c14.svg
paper_authors.fullname: Heajun An
paper_authors.type: user
paper_authors.user: aneverfull
paper_authors._id: 67fe093302ae092e4306af16
paper_authors.name: Sarvech Qadir
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-15T08:32:54.912Z
paper_authors._id: 626e75252c2c6d44b30b1523
paper_authors.avatarUrl: /avatars/458102026030f20af5e8c3c34c9b598c.svg
paper_authors.fullname: Sarvech Qadir
paper_authors.type: user
paper_authors.user: sarvech123
paper_authors._id: 67fe093302ae092e4306af17
paper_authors.name: Qi Zhang
paper_authors._id: 67fe093302ae092e4306af18
paper_authors.name: Pamela J. Wisniewski
paper_authors._id: 67fe093302ae092e4306af19
paper_authors.name: Jin-Hee Cho
paper_authors._id: 67fe093302ae092e4306af1a
paper_authors.name: Sang Won Lee
paper_authors._id: 67fe093302ae092e4306af1b
paper_authors.name: Ruoxi Jia
paper_authors._id: 67fe093302ae092e4306af1c
paper_authors.name: Lifu Huang
paper_title: LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety
    in Large Language Models
paper_summary: 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.
paper_ai_summary: A systematic investigation of the safety risks and unethical influence of large language model-driven persuasion reveals significant concerns across multiple strategies and factors like personality traits.
paper_ai_keywords: Large Language Models LLMs unethical persuasion manipulation deception exploitation PersuSafety persuasion safety framework persuasion scene creation persuasive conversation simulation undesirable persuasion strategies personality traits external pressures