arXiv:2608. 02657v1 Announce Type: cross Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.
By Jianshuo Dong, Yiming Liu, Maosen Zhang, Nan Deng, Xu Peng, Xiaoping Zhang, Tianwei Zhang, Jie Zhang, Han Qiu
arXiv:2608.02657v2 Announce Type: replace-cross
Abstract: Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While man...
By Jianshuo Dong, Yiming Liu, Maosen Zhang, Nan Deng, Peng Xu, Xiaoping Zhang, Tianwei Zhang, Jie Zhang, Han Qiu
arXiv:2505. 14300v2 Announce Type: replace Abstract: White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure safe model behavior.
By Maheep Chaudhary, Fazl Barez
arXiv:2607. 15550v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents have demonstrated remarkable capabilities in automating complex tasks, yet they introduce critical safety risks where a single erroneous action can lead to irreversible consequences.
By Xue Yu, Bo Yuan, Pengshuai Yang, Kailin Zhao, Hong Hu, Junlan Feng
arXiv:2609.36490v1 Announce Type: cross
Abstract: Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However,...
By Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
GUI-JEPA introduces a decoder‑free policy for mobile GUI agents that uses a Joint‑Embedding Predictive Architecture (JEPA) to generate action candidates directly in latent space. By predicting a latent representation for each candidate action, the model scores all options in a single batched pass without token decoding, dramatically reducing computation. Experiments on AndroidControl show that GUI‑JEPA is 2.8–4.0× faster than a 7B‑scale VLM agent while retaining 86–92% of its accuracy with only 6% of the parameters.
By Bingchao Wang, Yongcheng Jing, Shunyu Liu, Chengyu Wang, Jun Huang, Jie Yang, Wei Liu, Dacheng Tao