TimeGuard defends time series forecasting against backdoors via channel-wise pool training initialized by time-aware criteria and expanded with distance-regularized loss selection, improving poisoned MAE by 1.96x while keeping clean MAE within 5%.
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CBV generates clean-label poisoned samples for VLMs using diffusion models with score modification, multimodal guidance, and GradCAM-guided masks, achieving over 80% attack success rate on MSCOCO and VQA v2 while preserving normal functionality.
ATAAT is an adaptive adversarial tuning method that enables effective, stealthy backdoor attacks on VLA models by dynamically selecting gradient decoupling strategies based on attacker capabilities.
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CBV: Clean-label Backdoor Attacks on Vision Language Models via Diffusion Models
CBV generates clean-label poisoned samples for VLMs using diffusion models with score modification, multimodal guidance, and GradCAM-guided masks, achieving over 80% attack success rate on MSCOCO and VQA v2 while preserving normal functionality.