5 citations · 5 across the 13 of their papers we have counts for
14 papers
MATE: Policy-Aware Security Auditing for Mobile Agents via Synthesis-Driven Trajectory Learning
Changyue Jiang, Jiayi Wang, Xin Wen +3
Mobile agents powered by foundation models now automate complex, multi-step workflows on real devices, but their trajectories can violate app-specific security policies. Existing t…
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…
AgentGuard: An Attribute-Based Access Control Framework for Tool-Use LLM-Based Agent
Jiaqi Luo, Songyang Peng, Jiarun Dai +6
LLM-based agents have recently attracted significant attention due to their ability to autonomously invoke relevant tools to accomplish complex tasks. However, recent studies have…
MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction
Wenqi Zhang, Yulin Shen, Changyue Jiang +3
Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user interfaces (GUIs) to perform c…
WebTrap Park: An Automated Platform for Systematic Security Evaluation of Web Agents
Xinyi Wu, Jiagui Chen, Geng Hong +4
Web Agents are increasingly deployed to perform complex tasks in real web environments, yet their security evaluation remains fragmented and difficult to standardize. We present We…
When Bots Take the Bait: Exposing and Mitigating the Emerging Social Engineering Attack in Web Automation Agent
Xinyi Wu, Geng Hong, Yueyue Chen +5
Web agents, powered by large language models (LLMs), are increasingly deployed to automate complex web interactions. The rise of open-source frameworks (e.g., Browser Use, Skyvern-…
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…
Shell or Nothing: Real-World Benchmarks and Memory-Activated Agents for Automated Penetration Testing
Wuyuao Mai, Geng Hong, Qi Liu +5
Penetration testing is critical for identifying and mitigating security vulnerabilities, yet traditional approaches remain expensive, time-consuming, and dependent on expert human…
Large language model-powered AI systems achieve self-replication with no human intervention
Xudong Pan, Jiarun Dai, Yihe Fan +3
Self-replication with no human intervention is broadly recognized as one of the principal red lines associated with frontier AI systems. While leading corporations such as OpenAI a…
Frontier AI systems have surpassed the self-replicating red line
Xudong Pan, Jiarun Dai, Yihe Fan +1
Successful self-replication under no human assistance is the essential step for AI to outsmart the human beings, and is an early signal for rogue AIs. That is why self-replication…
Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks
Qifan Xiao, Xudong Pan, Yifan Lu +3
Automated driving systems rely on 3D object detectors to recognize possible obstacles from LiDAR point clouds. However, recent works show the adversary can forge non-existent cars…