Beyond Native Success: Auditing Deployment-Interface Exposure of CLIP Backdoors
Contrastive Language-Image Pre-training models are widely reused across downstream interfaces, including feature extraction, retrieval, reranking, and selection. Existing CLIP backdoor, however, usually validate attacks on a small attack-native task, leaving unclear whether the same poisoned checkpoint remains exposed, weakens, or becomes not applicable when reused through other interfaces. We introduce DIFE, a Deployment-Interface Footprint Evaluation framework that audits backdoored CLIP checkpoints across deployment interfaces. DIFE makes various evaluations comparable by specifying each interface's component readout, trigger channel, target event, reference condition, and metric. DIFE...
| Authors | Kunlan Xiang, Haomiao Yang, Wenbo Jiang |
|---|---|
| Primary category | cs.CR |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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