arXiv:2609.34790v2 Announce Type: replace-cross
Abstract: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boun...
By Hao Chen, Wenhui Dong, Ye Chen, Jiezhi Yao, Chenbo Xia, Yuwen Qu, Renxiang Wang, Fudong Yuan, Camil Hamami, Chenglong Pan, Xinquan Yue, Ziyu Wang, Fengyu Ye, Chenyang Si, Caifeng Shan
ActGov is a runtime enforcement framework that validates each action proposed by a large language model (LLM) agent before it interacts with external tools, ensuring that actions stay within task‑scoped authorization boundaries and comply with dynamically constructed policies. It builds policies from tool specifications, benign tasks, and failure traces, verifying updates via SMT‑based counterexample checking. In evaluations on AgentDojo and AgentDyn benchmarks, ActGov consistently reduces indirect prompt‑injection attack success while maintaining task utility, outperforming existing defenses.
By Kaiyuan Zhang, Yuke Peng, Ke Jiang, Yinqian Zhang
arXiv:2608. 03499v1 Announce Type: new Abstract: Recent advances in persistent personal-agent frameworks are making human-centered agent networks realistic deployment targets: each user can be served by an AI agent that acts on the user's behalf, maintains state, and communicates with other agents through social and task relations.
By Prince Zizhuang Wang, Aojie Yuan, Haiyue Zhang, Xiyang Hu, Yue Zhao, Shuli Jiang
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence subsequent actions and exceed user authorization. Existing...
arXiv:2606. 30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.
By Peizhi Niu, Wenjie Qu, Shangding Gu, Tianneng Shi, Yuankai Li, Ahmad Tawaha, Hend Alzahrani, Vincent Siu, Boyi Li, Chenguang Wang, Jiaheng Zhang, Basel Alomair, Ming Jin, Muhao Chen, Chi Wang, Costas Spanos, Dawn Song
Agent Memory Is a Surface for Endogenous Authorization Laundering explores how long‑running LLM agents use persistent memory to track permissions, restrictions, and revocations. The paper shows that when memory misrepresents evolving authorization states, agents can grant themselves authority that the underlying history never permitted, a phenomenon the authors call endogenous authorization laundering. To study this, the authors introduce EAL‑Bench, evaluate several LLMs across domains, and find that memory writers can create false authority in up to 50.2% of cases, which executors then act upon in 98.6% of trials. Two safeguards—requiring stored permissions to be backed by valid source events and tracking permission changes through bounded event sourcing—reduce laundering but also reject more legitimate actions, highlighting a safety‑utility tradeoff.
By Tommaso Cerruti, Mika Okamoto, Ansel Kaplan Erol