28 citations · 28 across the 15 of their papers we have counts for
9 papers · 1 filter
Narratives Mislead: An Empirical Study of How LLMs Misjudge CVSS Severity in Bug Bounty Reports
Ruitao Feng, Yuekang Li, Yao Zhang
Bug bounty platforms receive vulnerability reports faster than vendors can triage them, and Large Language Models (LLMs) are increasingly considered for assigning the Common Vulner…
AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation
Junchen Ding, Jialiang Dong, Yichen Zhu +5
The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the uncheck…
MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
Yue Wang, Yi Liu, Gelei Deng +4
Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a direct distribution channel fo…
PUFFERDOS: Efficient and Effective Attack String Generation for Regular Expression Denial of Service Vulnerabilities
Shangzhi Xu, Ziqi Ding, Xiao Cheng +5
ReDoS attacks constitute a critical class of resource-exhaustion vulnerabilities. In such attacks, adversaries exploit the pathological worst-case execution behavior of regular exp…
SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents
Yubin Qu, Yi Liu, Gelei Deng +4
A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. We cal…
MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content
Ruoqi Guo, Yi Liu, Gelei Deng +7
Mobile graphical user interface (GUI) agents driven by vision-language models (VLMs) perceive the screen as rendered pixels and choose actions from what they see, so they cannot re…
How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Study
Zhihao Chen, Ying Zhang, Yi Liu +7
Large Language Model (LLM) agents increasingly rely on third-party skills that operate within privileged execution environments and routinely handle sensitive credentials, yet how…
"Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild
Yi Liu, Zhihao Chen, Yanjun Zhang +4
LLM-based coding agents increasingly rely on third-party extensions called skills, which bundle natural language instructions and helper scripts that execute with full user privile…
Demystifying RCE Vulnerabilities in LLM-Integrated Apps
Tong Liu, Zizhuang Deng, Guozhu Meng +2
LLMs show promise in transforming software development, with a growing interest in integrating them into more intelligent apps. Frameworks like LangChain aid LLM-integrated app dev…