arXiv:2608. 04192v1 Announce Type: cross Abstract: Closed source agent skills may encode proprietary instructions, scripts, constants, and data.
By Peichun Hua, Haoxuan Xu, Mengyuan Li
The paper introduces AgentLeak, a black‑box attack that clones the task‑solving capabilities of a strong LLM agent onto a weaker one by exploiting differences between successful and failed executions. Unlike prior skill‑stealing methods that only recover explicit skill artifacts, AgentLeak identifies and incorporates missing procedural behaviors, boosting task pass rates by over 40% and closing more than 80% of the capability gap across 20 scenarios. The study demonstrates that observable execution behavior can leak proprietary procedural knowledge, posing a confidentiality risk for LLM agents.
By Xiaoting Lyu, Yuhong Wu, Yufei Han, Shichang Liu, Liang Zhang, Bin Wang, Bin Wang, Xiaobo Ma, Wei Wang
arXiv:2610.01564v1 Announce Type: cross
Abstract: LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowi...
By Tian Dong, Zixuan Ma, Haodong Zhao, Huaien Zhang, Shaofeng Li, Hao Chen
arXiv:2606. 01567v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable skills i.
By Yoshinari Fujinuma, Varun Gangal, Traian Rebedea, Makesh Narasimhan Sreedhar, Prasoon Varshney, Rebecca Qian, Anand Kannappan
Repeat-After-Me is a black-box adaptive visual prompt injection technique that can reveal personally identifiable information or trigger malicious tool calls in both open-weight and commercial vision‑language models, achieving attack success rates above 80% on Qwen3.6‑27B and 47% on GPT‑5.5. The method works even when the benign user prompt is unrelated to the injected task and does not explicitly authorize it, and it retains significant effectiveness when transferred across models or optimized on surrogate systems. In a real‑world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling remote code execution and secret exfiltration.
By Sizhe Chen, Yu-Lin Tsai, Ivan Evtimov, Kamalika Chaudhuri, Raluca Ada Popa, David Wagner, Arman Zharmagambetov
arXiv:2609.14987v1 Announce Type: cross
Abstract: Large language model (LLM) agents interact with external environments through tool invocation, but tool outputs can also expose them to indirect prom...
By Bingzheng Wang, Xiaoyan Gu, Wentao Wang, Xingyou Yang, Hongcheng Li, Rong Yin