FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models

• URL: http://arxiv.org/abs/2509.19870v1
• Date: Wed, 24 Sep 2025 08:15:28 GMT
• Title: FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
• Authors: Xin Wang, Jie Li, Zejia Weng, Yixu Wang, Yifeng Gao, Tianyu Pang, Chao Du, Yan Teng, Yingchun Wang, Zuxuan Wu, Xingjun Ma, Yu-Gang Jiang,
• Abstract summary: Vision-Language-Action (VLA) models are driving rapid progress in robotics.<n> adversarial images can "freeze" VLA models and cause them to ignore subsequent instructions.<n>FreezeVLA generates and evaluates action-freezing attacks via min-max bi-level optimization.
• Score: 124.02734355214325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their safety and robustness against adversarial attacks remain largely underexplored. In this work, we identify and formalize a critical adversarial vulnerability in which adversarial images can "freeze" VLA models and cause them to ignore subsequent instructions. This threat effectively disconnects the robot's digital mind from its physical actions, potentially inducing inaction during critical interventions. To systematically study this vulnerability, we propose FreezeVLA, a novel attack framework that generates and evaluates action-freezing attacks via min-max bi-level optimization. Experiments on three state-of-the-art VLA models and four robotic benchmarks show that FreezeVLA attains an average attack success rate of 76.2%, significantly outperforming existing methods. Moreover, adversarial images generated by FreezeVLA exhibit strong transferability, with a single image reliably inducing paralysis across diverse language prompts. Our findings expose a critical safety risk in VLA models and highlight the urgent need for robust defense mechanisms.

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