Abstract
Visual prompting (VP) is a new technique that adapts well-trained frozen models for source domain tasks to target domain tasks. This study examines VP's benefits for black-box model-level backdoor detection. The visual prompt in VP maps class subspaces between source and target domains. We identify a misalignment, termed class subspace inconsistency, between clean and poisoned datasets. Based on this, we introduce \textsc\{BProm\}, a black-box model-level detection method to identify backdoors in suspicious models, if any. \textsc\{BProm\} leverages the low classification accuracy of prompted models when backdoors are present. Extensive experiments confirm \textsc\{BProm\}'s effectiveness.