Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant until triggered, jailbreaks subvert safety alignment, and prompt injections override the deployer's instructions. Existing runtime defenses address these threats one at a time and often assume a clean reference model, trigger knowledge, or editable weights, assumptions that rarely hold for opaque third-party artifacts. We introduce Layerwise Convergence Fingerprinting (LCF), a tuning-free runtime monitor that treats the inter-layer hidden-state trajectory as a health signal: LCF computes a diagonal Mahalanobis distance on every inter-layer diffe...
| Authors | Nay Myat Min, Long H. Pham, Jun Sun |
|---|---|
| Primary category | cs.CR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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