From the 2 of 6 linked papers with an AI index.
6 papers
FlowGuard: From Signals to Evidence for MCP Security Detection
Baichao An, Pei Chen, Geng Hong +2
The paper introduces FlowGuard, a system that detects security risks in Model Context Protocol (MCP) interactions between LLM agents and external tools by combining semantic risk a…
Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability
Pei Chen, Baichao An, Mengying Wu +6
The paper introduces MCPZoo, a large collection of over 64 k Model Context Protocol (MCP) servers, and uses it to evaluate the reliability of existing security scanners for MCP ser…
PRISON: Unmasking the Criminal Potential of Large Language Models
Xinyi Wu, Geng Hong, Pei Chen +3
As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research overlooked the systematic understanding and assessm…
Unveiling the Resilience of LLM-Enhanced Search Engines against Black-Hat SEO Manipulation
Pei Chen, Geng Hong, Xinyi Wu +6
The emergence of Large Language Model-enhanced Search Engines (LLMSEs) has revolutionized information retrieval by integrating web-scale search capabilities with AI-powered summari…
MCPZoo: A Large-Scale Dataset of Runnable Model Context Protocol Servers for AI Agent
Mengying Wu, Pei Chen, Geng Hong +6
Model Context Protocol (MCP) enables agents to interact with external tools, yet empirical research on MCP is hindered by the lack of large-scale, accessible datasets. We present M…
You Can't Eat Your Cake and Have It Too: The Performance Degradation of LLMs with Jailbreak Defense
Wuyuao Mai, Geng Hong, Pei Chen +5
With the rise of generative large language models (LLMs) like LLaMA and ChatGPT, these models have significantly transformed daily life and work by providing advanced insights. How…