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
arXiv:2609.39178v1 Announce Type: cross
Abstract: Recently, Vision-Language-Action (VLA) models have revolutionized robotic manipulation by seamlessly integrating visual perception, language understa...
By Songhua Yang, Ziyu Liu, Yuanwei Liu, Xuetao Li, Xuanye Fei, He Huang, Zheng Wang, Miao Li
The paper investigates how adversarial patches affect Vision‑Language‑Action (VLA) policies, revealing that such patches can cause immediate action corruption and persistent state effects that linger after the patch is removed. A state‑restoration protocol is introduced to isolate these effects by removing the patch at action‑chunk boundaries and measuring recoverability within the remaining step budget. Experiments on OpenVLA-OFT with EDPA attacks show that only 36.2% of episodes recover after five chunks, whereas controls recover at 89.9% and 87.0%. A recovery adapter trained on attack‑induced states improves recovery from 7.7% to 47.4% at one‑chunk latency, but its effectiveness drops sharply with delayed intervention, underscoring the importance of timely recovery.
Adversarial patches applied to Vision‑Language‑Action (VLA) policies not only corrupt actions immediately but also leave lasting state effects that persist after the patch is removed. The study introduces a state‑restoration protocol that evaluates recoverability after patch removal, distinguishing true adversarial impact from occlusion or action‑error magnitude. Experiments on OpenVLA‑OFT with EDPA attacks show that only 36.2% of episodes recover after five chunks, while controls recover at much higher rates; a recovery adapter can improve recovery but its effectiveness drops with delayed intervention.
By Enhao Wu, Fusen Guo, Yuxin Cao, Ziyang Lyu, Lin Li, Wei Song
arXiv:2607. 00174v1 Announce Type: cross Abstract: We present a black-box model-stealing attack that recovers private vision-tokenizer configurations of deployed vision-language models (VLMs), including the visual patch size and input preprocessing pipeline.
By Kai Hu, Akash Bharadwaj, Weichen Yu, Matt Fredrikson
Recent advancements in Image-to-Video (I2V) generation have transformed input images from simple appearance references into interactive control interfaces where visual cues such as arrows, sketches, and emojis orchestrate complex video dynamics with unprecedented controllability. However, these seemingly innocuous static cues can be interpreted by models as executable temporal instructions, unfolding into harmful actions in the generated videos.