LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety
  in Large Language Models
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.
67fe093302ae092e4306af12
Minqian Liu
admin_assigned
2025-04-15T08:32:23.136Z
64c32a75d15a8812b71afc48
/avatars/4186c592b00aea73fb8c5bb719935ce7.svg
Minqian Liu
user
mqliu
67fe093302ae092e4306af13
Zhiyang Xu
admin_assigned
2025-04-15T08:32:36.033Z
64b6c686cf5117d7962d8f62
/avatars/96ed7a9602aa4c21b3a3d89608e76dc8.svg
Zhiyang Xu
user
Zhiyang03
67fe093302ae092e4306af14
Xinyi Zhang
67fe093302ae092e4306af15
Heajun An
admin_assigned
2025-04-15T08:32:49.203Z
679d30bf48f48796199c415e
/avatars/c28e0dd7c9f6f62bccdd8eb2c8772c14.svg
Heajun An
user
aneverfull
67fe093302ae092e4306af16
Sarvech Qadir
admin_assigned
2025-04-15T08:32:54.912Z
626e75252c2c6d44b30b1523
/avatars/458102026030f20af5e8c3c34c9b598c.svg
Sarvech Qadir
user
sarvech123
67fe093302ae092e4306af17
Qi Zhang
67fe093302ae092e4306af18
Pamela J. Wisniewski
67fe093302ae092e4306af19
Jin-Hee Cho
67fe093302ae092e4306af1a
Sang Won Lee
67fe093302ae092e4306af1b
Ruoxi Jia
67fe093302ae092e4306af1c
Lifu Huang
LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety
  in Large Language Models
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.
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.
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