Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent
  Approach
Authors: name: Yuchen Wu, name: Edward Sun, name: Kaijie Zhu, name: Jianxun Lian, name: Jose Hernandez-Orallo, name: Aylin Caliskan, name: Jindong Wang

Large language models (LLMs) typically generate identical or similar
responses for all users given the same prompt, posing serious safety risks in
high-stakes applications where user vulnerabilities differ widely. Existing
safety evaluations primarily rely on context-independent metrics - such as
factuality, bias, or toxicity - overlooking the fact that the same response may
carry divergent risks depending on the user's background or condition. We
introduce personalized safety to fill this gap and present PENGUIN - a
benchmark comprising 14,000 scenarios across seven sensitive domains with both
context-rich and context-free variants. Evaluating six leading LLMs, we
demonstrate that personalized user information significantly improves safety
scores by 43.2%, confirming the effectiveness of personalization in safety
alignment. However, not all context attributes contribute equally to safety
enhancement. To address this, we develop RAISE - a training-free, two-stage
agent framework that strategically acquires user-specific background. RAISE
improves safety scores by up to 31.6% over six vanilla LLMs, while maintaining
a low interaction cost of just 2.7 user queries on average. Our findings
highlight the importance of selective information gathering in safety-critical
domains and offer a practical solution for personalizing LLM responses without
model retraining. This work establishes a foundation for safety research that
adapts to individual user contexts rather than assuming a universal harm
standard.