5 citations · 10 across the 19 of their papers we have counts for
9 papers · 1 filter
Rethinking MCP Security: A Large-Scale Study of Runtime MCP Servers and Security Scanner Reliability
Pei Chen, Baichao An, Mengying Wu +6
The Model Context Protocol (MCP) has rapidly established itself as a standard interface for enabling LLM-based agents to interact with external tools and services. As MCP servers a…
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
Jiaqi Luo, Jiarun Dai, Zhile Chen +8
Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must…
AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges
Fengyu Liu, Jiarun Dai, Yihe Fan +11
Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation. However, evaluating their offensive c…
CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly
Yihe Fan, Changyi Li, Lichen Xu +4
LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to adapt across diverse targets and…
Invisible Threats from Model Context Protocol: Generating Stealthy Injection Payload via Tree-based Adaptive Search
Yulin Shen, Xudong Pan, Geng Hong +1
Recent advances in the Model Context Protocol (MCP) have enabled large language models (LLMs) to invoke external tools with unprecedented ease. This creates a new class of powerful…
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…