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When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers

2026-08-12 22:11:47
Yang Liu, Ran Zou
arXiv_CL
       
Abstract

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 evaluate Unsupported Groundedness, a multi-probe escalation for explanation-camouflage stress cases. Unsupported Groundedness improves signals but does not close the adaptive gap.

Abstract (translated)

URL

https://arxiv.org/abs/2608.12623

PDF

https://arxiv.org/pdf/2608.12623.pdf

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