When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers
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Computer Science > Computation and Language
Title:When Explanations Betray Backdoors: Black-Box Auditing for Language Model Classifiers
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.
| Comments: | 16 pages, 1 figure |
| Subjects: | Computation and Language (cs.CL); Machine Learning (stat.ML) |
| Cite as: | arXiv:2608.12623 [cs.CL] |
| (or arXiv:2608.12623v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12623
arXiv-issued DOI via DataCite (pending registration)
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