Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies
LLMs internalize safety policies through RLHF, yet these policies are never formally specified and remain difficult to inspect. Existing benchmarks evaluate models against external standards but do not measure whether models understand and enforce their own stated boundaries. We introduce the Symbolic-Neural Consistency Audit (SNCA), a framework that (1) extracts a model's self-stated safety rules via structured prompts, (2) formalizes them as typed predicates (Absolute, Conditional, Adaptive), and (3) measures behavioral compliance via deterministic comparison against harm benchmarks. Evaluating four frontier models across 45 harm categories and 47,496 observations reveals systematic gap...
| Authors | Avni Mittal |
|---|---|
| Primary category | cs.CL |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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