arXiv AI June 10, 2026 By Zi Yin, Peilin Chai, Siyuan Huang, Zhanhao Hu

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

arXiv:2606. 10371v1 Announce Type: cross Abstract: Diffusion-based action generation has become a foundational component of embodied AI, but its reliance on visual conditioning leaves deployed visuomotor policies vulnerable to adversarial manipulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control

arXiv:2606. 16605v1 Announce Type: new Abstract: World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making.

By Junjian Zhang, Hao Tan, Ruonan Li, Dong Zhu, Aiping Li, Zhaoquan Gu

agents robotics benchmarks safety

arXiv Machine Learning

Jul 17

BadWAM: When World-Action Models Dream Right but Act Wrong

arXiv:2607. 15207v1 Announce Type: new Abstract: World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction.

By Qi Li, Xingyi Yang, Xinchao Wang

robotics safety

arXiv AI

Aug 14

UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models

arXiv:2608. 13453v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wide range of manipulation tasks.

By Yukun Dai, Mingzhe Dai, Tianshi Wang, Fengling Li, Jingjing Li, Lei Zhu

agents robotics multimodal safety

arXiv AI

Aug 12

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

arXiv:2608. 10393v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in controlling robots across diverse manipulation tasks.

By Jiahui Han, Yuhui Yao, Xin Wang, Jiafei Cao, Mingxuan Zhang, Danfeng Shan, Huiqi Deng, Guanchu Wang, Xia Hu

diffusion robotics multimodal safety

arXiv Machine Learning

Aug 5

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

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.

By Hoseong Tae, Jong-Seok Lee

rag robotics multimodal safety

arXiv AI

Sep 1

SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems

arXiv:2402.03741v4 Announce Type: replace-cross Abstract: Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast application prospects, such as swarm control of drones,...

By Oubo Ma, Yuwen Pu, Linkang Du, Yang Dai, Ruo Wang, Xiaolei Liu, Yingcai Wu, Shouling Ji

agents reinforcement-learning robotics safety

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Related stories

ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control

BadWAM: When World-Action Models Dream Right but Act Wrong

UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models

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

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

SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems