VulnSafe: Measuring Vulnerability-Aware Relational Safety in Large Language Models

Emotionally supportive interactions require large language models not only to avoid overtly harmful content, but also to recognize vulnerability-related risk, regulate their relational stance, preserve user agency, and provide context-appropriate support. Existing safety benchmarks often measure isolated violations, leaving unclear what underlying response competence their scores represent. We introduce VulnSafe, a Chinese capability assessment centered on Vulnerability-Aware Response Competence (VARC). VARC organizes relational safety into five complementary constructs: Risk Perception, Safety Preservation, Autonomy Protection, Supportive Response, and Boundary Management. Following an evidence-centered design, 41 scenarios elicit construct-relevant behavior through 2,050 balanced prompts and scenario-specific scoring rubrics. The assessment further separates proactive should-do behavior from prohibitive should-not behavior. Across eight representative LLMs, results show distinct competence profiles and larger disparities in proactive requirements than in prohibitive requirements. VulnSafe therefore makes relational safety interpretable as a multidimensional model capability rather than as refusal compliance alone.