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.

翻译：视觉Transformer（ViT）的广泛采用加剧了第三方模型库的供应链风险，攻击者可能向发布的模型检查点中植入后门。现有的ViT后门攻击主要依赖于投毒数据训练，而先前无需数据的尝试通常需要合成数据微调或额外的模型组件。本文提出无需数据的逻辑门控后门攻击（DF-LoGiT），这是一种通过直接权重编辑实现的、真正无需数据的ViT后门攻击方法。DF-LoGiT利用ViT固有的多头注意力架构，实现了一种逻辑门控组合触发器，从而构建出隐蔽且有效的后门。我们通过理论分析和大量实验验证了其有效性，结果表明DF-LoGiT在几乎不影响良性任务准确率的情况下实现了接近100%的攻击成功率，并且能够有效抵御经典的及ViT特有的代表性防御方法。