AI RESEARCH
Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models
arXiv CS.CV
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ArXi:2512.22046v2 Announce Type: replace Prompt-driven Video Segmentation Foundation Models (VSFMs), such as SAM2, are increasingly used in applications including autonomous driving and digital pathology, yet their security risks remain underexplored. We study backdoor attacks against VSFMs and show that directly applying classic attacks such as BadNet is largely ineffective, yielding attack success rates (ASR) below 5%. Through gradient-similarity and attention-map analyses, we find that traditional backdoor.