LCF detects multiple LLM runtime threats by computing aggregated diagonal Mahalanobis distances on layer-wise hidden-state differences, calibrated on clean examples, achieving high detection rates with low overhead across several model architectures.
At λ=5.0, VPI becomes the most vulnerable (76.5% residual), suggesting this trigger type has a sharper transition between detectable and evasive regimes
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Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
LCF detects multiple LLM runtime threats by computing aggregated diagonal Mahalanobis distances on layer-wise hidden-state differences, calibrated on clean examples, achieving high detection rates with low overhead across several model architectures.