arXiv:2605. 01143v2 Announce Type: replace Abstract: Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning.
By Sheldon Yu, Yingcheng Sun, Hanqing Guo, Qianqian Tong
arXiv:2508.14925v2 Announce Type: replace-cross
Abstract: By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a...
By Zhiqiang Wang, Yichao Gao, Yanting Wang, Suyuan Liu, Haifeng Sun, Haoran Cheng, Guanquan Shi, Haohua Du, Xiangyang Li
arXiv:2503. 15560v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to sophisticated multi-turn manipulation attacks, where adversaries strategically build context through seemingly benign conversational turns to circumvent safety measures and elicit harmful or unauthorized responses.
By Prashant Kulkarni, Assaf Namer
arXiv:2607. 25297v1 Announce Type: cross Abstract: The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks.
By Ping He, Yuexiang Xie, Yaliang Li, Shouling Ji
The paper introduces the Agent Cross‑Layer Evidence (ACE) corpus, pairing application‑level telemetry with kernel‑level syscall traces to study agent security. It shows that kernel evidence alone is discriminative and that combining it with application‑level data outperforms either layer alone, revealing complementary signals. The study also demonstrates that this cross‑layer approach generalizes to unseen attack families and works across different agent runtimes.
By Spencer King, Zhilu Zhang, Mikhail Kuznetsov, Kay Liu, Baris Coskun, Wei Ding
arXiv:2606. 20470v1 Announce Type: cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.
By Reza Soosahabi, Vivek Namsani