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20232026
most citedFrontier AI systems have surpassed the self-replicating red line

5 citations · 5 across the 13 of their papers we have counts for

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10 papers · 1 filter

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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-…

cs.CR2025

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…

cs.CR2025

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…

cs.CR2023

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…