This research introduces U-SAFEBENCH, a novel benchmark for evaluating how LLMs adapt safety responses to different user profiles, rather than applying universal standards.

• Evaluated 18 LLMs on their ability to provide user-appropriate safety responses
• Found significant safety gaps when dealing with different demographic profiles
• Demonstrated most current LLMs fail to adjust safety protocols for specific user needs
• Identified which models best adapt to user-specific contexts

For security professionals, this research highlights a critical vulnerability in current AI safety approaches—standardized safety measures may protect some users while failing others based on their profiles, preferences, and needs.

Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models