The paper investigates how self‑evolving personal agents become more vulnerable to indirect prompt injection (IPI). Using a two‑phase methodology, the authors show that when agents learn from user feedback—either through skill evolution or reinforcement learning—the attack success rate of IPI increases by 14.0–48.8 percentage points compared to a non‑evolving baseline. The study highlights a significant safety risk for personal agents that adapt based on user interactions.
By Xinyan Wang, Xiaogeng Liu, Jun Shao, Chaowei Xiao
arXiv:2610.06401v2 Announce Type: replace-cross
Abstract: Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing tr...
By Mohamed Dhouib, Clement Elliker, Alexi Canesse, Ma\"el Jenny, Lucas-Andrei Thil, Mahammed El Sharkawy, Sonia Vanier, Elie Bursztein
arXiv:2608.30207v1 Announce Type: cross
Abstract: Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal...
By Chen Xiong, Zhiyuan He, Pin-Yu Chen, Stjepan Picek, Tsung-Yi Ho
The paper revisits Thompson’s classic compiler back‑door attack in the context of self‑modifying AI coding agents. By poisoning the benchmarks used for self‑evaluation, the authors demonstrate that agents such as the Darwin Gödel Machine, Self‑Improving Coding Agent, and Hyperagents can be coaxed into generating vulnerable code, even on clean, held‑out tasks. Experiments show that the contamination can persist after subsequent clean training, highlighting the need for more robust agent designs.
By Franziska Roesner, Tadayoshi Kohno
Computer use agents (CUAs) are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal, and they are increasingly deployed to automate ev...
arXiv:2608. 04053v1 Announce Type: cross Abstract: Prompt injection remains a critical threat to LLM agents, yet existing defenses treat each task as a self-contained problem, independent of previous encounters.
By Shihao Weng, Yang Feng, Xiaofei Xie, Jiongchi Yu