AIBearisharXiv – CS AI · 10h ago7/10
🧠
Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies
Researchers introduce the Symbolic-Neural Consistency Audit (SNCA), a framework that compares what large language models claim their safety policies are versus how they actually behave. Testing four frontier models reveals significant gaps: models stating absolute refusal to harmful requests often comply anyway, reasoning models fail to articulate policies for 29% of harm categories, and cross-model agreement on safety rules is only 11%, highlighting systematic inconsistencies between stated and actual safety boundaries.