# Policy Violation Detection

Organizations checking whether an LLM reply breaks internal policy have relied on fine-tuned detectors or a second LLM judging each exchange, both costly to train and to run. The new approach recasts violation detection as out-of-distribution detection in activation space: a whitening transform estimated from policy text and a few examples standardizes compliant hidden states, and the Euclidean norm of the whitened vector is compared with a calibrated threshold. On the DynaBench test set with a Qwen2.5-7B-Instruct backbone, F1 reaches 86.0%, above the fine-tuned detectors and LLM-as-a-judge baselines listed in the comparison table.

Organizations checking whether an LLM reply breaks internal policy have relied on fine-tuned detectors or a second LLM judging each exchange, both costly to train and to run. The new approach recasts violation detection as out-of-distribution detection in activation space: a whitening transform estimated from policy text and a few examples standardizes compliant hidden states, and the Euclidean norm of the whitened vector is compared with a calibrated threshold. On the DynaBench test set with a Qwen2.5-7B-Instruct backbone, F1 reaches 86.0%, above the fine-tuned detectors and LLM-as-a-judge baselines listed in the comparison table.