When Behavioral Safety Evaluation Fails: A Representation-Level Perspective
Safety evaluation of large language models (LLMs) is largely behavioral: a model is certified safe when it refuses harmful requests and answers benign ones. But refusing on the prompts an auditor happens to try does not show that the model is far from harmful behavior. Behavioral tests observe outputs; they do not measure how easily an intervention on the model turns a refusal into compliance. We call the gap between what static audits certify and what an intervention can reach the audit gap, and we show it is realizable: one can build a model that matches its safety-aligned base on every static audit yet gives way to a small, known perturbation of its internal state. We construct such di...
| Authors | Enyi Jiang, Anders Gjølbye, Yibo Jacky Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 June 2026 |
| Last updated at source | 4 August 2026 |
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