5 citations · 10 across the 29 of their papers we have counts for
18 papers · 1 filter
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…
One Click to Leak: Characterizing the Real-World Usage and Threat Impact of MNO-based Single Sign-On Websites
Jiasheng Huang, Mingxuan Liu, Pei Chen +7
Mobile Network Operator (MNO)-based Single Sign-On (MSSO) is a password-free authentication framework relying on mobile data sessions. Unlike traditional SSO, it shifts the Identit…
Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
Yuanmin Huang, Chen Chen, Geng Hong +5
Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an incre…
FlowGuard: From Signals to Evidence for MCP Security Detection
Baichao An, Pei Chen, Geng Hong +2
The Model Context Protocol (MCP) enables LLM agents to interact with external tools through metadata exchange, tool invocation, and response consumption. Existing MCP security scan…
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…
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…
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…
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…
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…
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…
Revealing the Black Box of Device Search Engine: Scanning Assets, Strategies, and Ethical Consideration
Mengying Wu, Geng Hong, Jinsong Chen +6
In the digital age, device search engines such as Censys and Shodan play crucial roles by scanning the internet to catalog online devices, aiding in the understanding and mitigatio…
Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications
Changyue Jiang, Xudong Pan, Geng Hong +3
Retrieval-Augmented Generation (RAG) expands the knowledge boundary of large language models (LLMs) by integrating external knowledge bases, whose construction is often time-consum…