Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of
  Large Language Models
As the use of large language model (LLM) agents continues to grow, their
safety vulnerabilities have become increasingly evident. Extensive benchmarks
evaluate various aspects of LLM safety by defining the safety relying heavily
on general standards, overlooking user-specific standards. However, safety
standards for LLM may vary based on a user-specific profiles rather than being
universally consistent across all users. This raises a critical research
question: Do LLM agents act safely when considering user-specific safety
standards? Despite its importance for safe LLM use, no benchmark datasets
currently exist to evaluate the user-specific safety of LLMs. To address this
gap, we introduce U-SAFEBENCH, the first benchmark designed to assess
user-specific aspect of LLM safety. Our evaluation of 18 widely used LLMs
reveals current LLMs fail to act safely when considering user-specific safety
standards, marking a new discovery in this field. To address this
vulnerability, we propose a simple remedy based on chain-of-thought,
demonstrating its effectiveness in improving user-specific safety. Our
benchmark and code are available at https://github.com/yeonjun-in/U-SafeBench.
67bbfc7ab3920fd18e63cb26
Yeonjun In
claimed_verified
2025-02-24T09:07:06.533Z
60af909e288a0f96f6cefc4d
/avatars/44a8ab48acb58c45de0b0947a1b56e7c.svg
Yeonjun In
user
Yeonjun
67bbfc7ab3920fd18e63cb27
Wonjoong Kim
claimed_verified
2025-02-24T13:05:06.940Z
67bc62b77727595ca5b6a4ca
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/JXilVx4ACCw6L9BnomqVP.png
Wonjoong Kim
user
wjkim0229
67bbfc7ab3920fd18e63cb28
Kanghoon Yoon
67bbfc7ab3920fd18e63cb29
Sungchul Kim
admin_assigned
2025-02-24T13:28:51.033Z
6250611d2f9acc6168e42737
/avatars/1e053d3fa387d81b45a2435e4a633ad1.svg
Sungchul Kim
user
subright
67bbfc7ab3920fd18e63cb2a
Mehrab Tanjim
admin_assigned
2025-02-24T13:28:37.445Z
6366e2d9575c93ceda0791d8
/avatars/a53cb1bb7cd9c63a2520587108ffe962.svg
Mehrab Tanjim
user
Mehrab-Tanjim
67bbfc7ab3920fd18e63cb2b
Kibum Kim
admin_assigned
2025-02-24T13:28:30.900Z
64b73688dcbce176037ef420
https://cdn-avatars.huggingface.co/v1/production/uploads/64b73688dcbce176037ef420/R-fcEaZz5vrY74QO5oclB.jpeg
Kibum Kim
user
kb-kim
67bbfc7ab3920fd18e63cb2c
Chanyoung Park
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of
  Large Language Models
As the use of large language model (LLM) agents continues to grow, their
safety vulnerabilities have become increasingly evident. Extensive benchmarks
evaluate various aspects of LLM safety by defining the safety relying heavily
on general standards, overlooking user-specific standards. However, safety
standards for LLM may vary based on a user-specific profiles rather than being
universally consistent across all users. This raises a critical research
question: Do LLM agents act safely when considering user-specific safety
standards? Despite its importance for safe LLM use, no benchmark datasets
currently exist to evaluate the user-specific safety of LLMs. To address this
gap, we introduce U-SAFEBENCH, the first benchmark designed to assess
user-specific aspect of LLM safety. Our evaluation of 18 widely used LLMs
reveals current LLMs fail to act safely when considering user-specific safety
standards, marking a new discovery in this field. To address this
vulnerability, we propose a simple remedy based on chain-of-thought,
demonstrating its effectiveness in improving user-specific safety. Our
benchmark and code are available at https://github.com/yeonjun-in/U-SafeBench.
The introduction of U-SAFEBENCH addresses the lack of benchmark datasets for evaluating user-specific safety standards in large language models, revealing that current models fail to meet these standards.
large language model LLM safety vulnerabilities benchmark datasets user-specific safety standards U-SAFEBENCH chain-of-thought