Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models

ArXi:2605.09005v1 Announce Type: cross Vision-Language-Action models (VLAs) generalist robotic control by enabling end-to-end decision policies directly from multi-modal inputs. As trained VLAs are increasingly shared and adapted, protecting model ownership becomes essential for secure deployment and responsible open-source usage. In this paper, we present GuardVLA, the first backdoor-based ownership verification framework specifically designed for VLAs. GuardVLA embeds a stealthy and harmless backdoor watermark into the protected model during.