# Conflict-Aware Evidence Verification

On the corrected CIC-IDS2017 dataset, when labels and confidences from four heterogeneous detectors are corrupted under a threat model, unverified open-weight LLM investigators fall to 0.50–0.72 accuracy with one corrupted detector and to 0.13 or less with three. Conflict-Aware Evidence Verification (CAV) keeps reported confidences out of the vote and, only when tools conflict, tests the leading candidates with a nearest-neighbour trust score computed from raw traffic features. With its prompt frozen before testing, CAV-LLM reaches 0.84–0.88 under three corrupted detectors in both persistent and sporadic scopes, against at most 0.08 for the naive investigator, while calling the LLM on 0.4% of clean traffic.

On the corrected CIC-IDS2017 dataset, when labels and confidences from four heterogeneous detectors are corrupted under a threat model, unverified open-weight LLM investigators fall to 0.50–0.72 accuracy with one corrupted detector and to 0.13 or less with three. Conflict-Aware Evidence Verification (CAV) keeps reported confidences out of the vote and, only when tools conflict, tests the leading candidates with a nearest-neighbour trust score computed from raw traffic features. With its prompt frozen before testing, CAV-LLM reaches 0.84–0.88 under three corrupted detectors in both persistent and sporadic scopes, against at most 0.08 for the naive investigator, while calling the LLM on 0.4% of clean traffic.