When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers
Language model classifiers with explanations are used for moderation, routing, topic triage, and low-resource annotation. We study black-box auditing when the defender has only clean calibration data without trigger information but can ask the classifier for a label plus a short rationale or quoted evidence. We introduce Groundedness Drift, a lightweight score measuring whether the answer summary remains grounded in the input. Across two 7B backbones, five datasets, and four common non-adaptive OpenBackdoor-style attack families, Groundedness Drift achieves higher AUROC and lower residual target ASR than every compared detector in all cases at a nominal 5\% clean-FPR budget. We then evalu...
| Authors | Yang Liu, Ran Zou |
|---|---|
| Primary category | cs.CL |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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