arXiv Machine Learning August 5, 2026 By Hoseong Tae, Jong-Seok Lee

DRIFT: Derailing Denoising Trajectories of Flow-Matching VLAs with Adversarial Patch Attack

Read the original on arXiv Machine Learning →

arXiv:2608. 03207v1 Announce Type: cross Abstract: Flow-matching vision-language-action (VLA) models such as pi0 generate robot actions by integrating a learned denoising velocity field, and have been reported to resist adversarial perturbations that readily fool autoregressive VLAs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies

Hidden in Plain Sight: Diffusion-Based Unrestricted Robotic Attacks on Vision-Language-Action Models

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