arXiv AI By Liwei Liu, Tianzhu Han, Zijian Liu, Zishu Dong, Na Ruan

ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP

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arXiv:2606. 27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become foundational to modern agent ecosystems.

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arXiv AI
Sep 12

No-Box Vulnerability Analysis: Description-only Detection of Indirect Prompt Injection Vulnerabilities in MCP Servers

The paper introduces no‑box vulnerability analysis, a method that detects security flaws without system access or runtime interaction by examining only the functionality metadata of a target. Using this approach, the authors built MCPSEC to audit Model Context Protocol servers for indirect prompt injection vulnerabilities, evaluating it on 20 servers with 177 tools. MCPSEC identified 143 vulnerable tools, achieving 98.9% recall of verified vulnerabilities, outperforming an LLM baseline.

By Zehua Zhang, Jie Hu, Pratham Hegde, Aditya Maheshbhai Gabani, Souradip Nath, Yibo Liu, Siyu Liu, Hongkai Chen, Hulin Wang, Zhuoer Lyu, Chang Zhu, Divij Handa, Yan Shoshitaishvili, Tiffany Bao, Ruoyu Wang, Adam Doupe
arXiv AI
Jun 12

PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections

arXiv:2606. 12737v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and environments, introducing new security risks such as indirect prompt injection attacks through untrusted external sources.

By Pengfei He, Lesly Miculicich, Vishesh Sharma, Ash Fox, George Lee, Jiliang Tang, Tomas Pfister, Long T. Le