Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent
  Approach
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.
6835fd8e869613d0fd6dc2fc
Yuchen Wu
extracted_pending
2025-05-27T17:59:44.169Z
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Yuchen Wu
user
wick1d
6835fd8e869613d0fd6dc2fd
Edward Sun
claimed_verified
2025-05-29T20:18:48.711Z
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/avatars/8d3b0eed5144ff452ca2a49a0f67d7c4.svg
Edward Sun
user
EdwardoSunny
6835fd8e869613d0fd6dc2fe
Kaijie Zhu
6835fd8e869613d0fd6dc2ff
Jianxun Lian
6835fd8e869613d0fd6dc300
Jose Hernandez-Orallo
6835fd8e869613d0fd6dc301
Aylin Caliskan
6835fd8e869613d0fd6dc302
Jindong Wang
claimed_verified
2025-06-02T07:49:05.005Z
6204cc0d522e40b4a18d86e2
/avatars/18daf2de5671e711dc745388dd60569d.svg
Jindong Wang
user
jindongwang
Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent
  Approach
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.
Introducing personalized safety for LLMs through PENGUIN and RAISE frameworks Enhances safety scores by leveraging user-specific information without retraining models.
large language models (LLMs) personalized safety PENGUIN context-rich context-free safety alignment RAISE user-specific background safety scores selective information gathering