Adversarial Attacks on VLA Models

Vision-language-action models turn camera observations and natural-language instructions into robot actions, so adversarial-attack research asks how small or hidden changes can derail grounding, planning, or control in embodied settings. Studies probe instruction edits and image perturbations, physical patches and textures, training-time backdoors, and faults in model weights or intermediate reasoning. Evaluations span manipulation simulators such as LIBERO, autonomous-driving scenarios, and real robot hardware; defenses include robust fine-tuning, runtime monitoring and recovery, and certified action or rollout guarantees. Work increasingly examines transferable, long-horizon, and adaptive threats, while a central challenge is improving robustness without sacrificing task-relevant perception or clean-task performance.