Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of
  Large Language Models
Authors: name: Yeonjun In, name: Wonjoong Kim, name: Kanghoon Yoon, name: Sungchul Kim, name: Mehrab Tanjim, name: Kibum Kim, name: Chanyoung Park

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.