title: Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of
    Large Language Models
summary: 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.
paper_authors._id: 67bbfc7ab3920fd18e63cb26
paper_authors.name: Yeonjun In
paper_authors.status: claimed_verified
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paper_authors.fullname: Yeonjun In
paper_authors.type: user
paper_authors.user: Yeonjun
paper_authors._id: 67bbfc7ab3920fd18e63cb27
paper_authors.name: Wonjoong Kim
paper_authors.status: claimed_verified
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paper_authors.fullname: Wonjoong Kim
paper_authors.type: user
paper_authors.user: wjkim0229
paper_authors._id: 67bbfc7ab3920fd18e63cb28
paper_authors.name: Kanghoon Yoon
paper_authors._id: 67bbfc7ab3920fd18e63cb29
paper_authors.name: Sungchul Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:28:51.033Z
paper_authors._id: 6250611d2f9acc6168e42737
paper_authors.avatarUrl: /avatars/1e053d3fa387d81b45a2435e4a633ad1.svg
paper_authors.fullname: Sungchul Kim
paper_authors.type: user
paper_authors.user: subright
paper_authors._id: 67bbfc7ab3920fd18e63cb2a
paper_authors.name: Mehrab Tanjim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:28:37.445Z
paper_authors._id: 6366e2d9575c93ceda0791d8
paper_authors.avatarUrl: /avatars/a53cb1bb7cd9c63a2520587108ffe962.svg
paper_authors.fullname: Mehrab Tanjim
paper_authors.type: user
paper_authors.user: Mehrab-Tanjim
paper_authors._id: 67bbfc7ab3920fd18e63cb2b
paper_authors.name: Kibum Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T13:28:30.900Z
paper_authors._id: 64b73688dcbce176037ef420
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b73688dcbce176037ef420/R-fcEaZz5vrY74QO5oclB.jpeg
paper_authors.fullname: Kibum Kim
paper_authors.type: user
paper_authors.user: kb-kim
paper_authors._id: 67bbfc7ab3920fd18e63cb2c
paper_authors.name: Chanyoung Park
paper_title: Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of
    Large Language Models
paper_summary: 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.
paper_ai_keywords: large language model (LLM) agents safety vulnerabilities extensive benchmarks safety standards user-specific standards user-specific safety U-SAFEBENCH chain-of-thought