DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908808052211712 |
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| author | Shen, Xiaozuo Cai, Yifei Ning, Rui Xin, Chunsheng Wu, Hongyi |
| author_facet | Shen, Xiaozuo Cai, Yifei Ning, Rui Xin, Chunsheng Wu, Hongyi |
| contents | The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors into released checkpoints. Existing ViT backdoor attacks largely rely on poisoned-data training, while prior data-free attempts typically require synthetic-data fine-tuning or extra model components. This paper introduces Data-Free Logic-Gated Backdoor Attacks (DF-LoGiT), a truly data-free backdoor attack on ViTs via direct weight editing. DF-LoGiT exploits ViT's native multi-head architecture to realize a logic-gated compositional trigger, enabling a stealthy and effective backdoor. We validate its effectiveness through theoretical analysis and extensive experiments, showing that DF-LoGiT achieves near-100% attack success with negligible degradation in benign accuracy and remains robust against representative classical and ViT-specific defenses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03040 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers Shen, Xiaozuo Cai, Yifei Ning, Rui Xin, Chunsheng Wu, Hongyi Cryptography and Security The widespread adoption of Vision Transformers (ViTs) elevates supply-chain risk on third-party model hubs, where an adversary can implant backdoors into released checkpoints. Existing ViT backdoor attacks largely rely on poisoned-data training, while prior data-free attempts typically require synthetic-data fine-tuning or extra model components. This paper introduces Data-Free Logic-Gated Backdoor Attacks (DF-LoGiT), a truly data-free backdoor attack on ViTs via direct weight editing. DF-LoGiT exploits ViT's native multi-head architecture to realize a logic-gated compositional trigger, enabling a stealthy and effective backdoor. We validate its effectiveness through theoretical analysis and extensive experiments, showing that DF-LoGiT achieves near-100% attack success with negligible degradation in benign accuracy and remains robust against representative classical and ViT-specific defenses. |
| title | DF-LoGiT: Data-Free Logic-Gated Backdoor Attacks in Vision Transformers |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2602.03040 |