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20242026
most citedBridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages

1 citations · 1 across the 17 of their papers we have counts for

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cs.SE2026

One Is Not Enough: The Untold Story of Multiple Security Patches for One Vulnerability

Fangyuan Zhang, Lyuye Zhang, Lingling Fan +6

Security patches (SPs) are the main mechanism for fixing software vulnerabilities, yet a single vulnerability is not always resolved by a single patch: fixes may be completed incre…

cs.SE2026

Benchmarking Automated Security Patch Backporting: How Far Are We?

Jincheng Yang, Yulong Fu, Chengwei Liu +5

Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their respective datasets. However, these eval…

cs.SE2026

Implicit, Yet Impactful: Understanding Hidden Dependencies in Java Projects

Lyuye Zhang, Chengwei Liu, Fangyuan Zhang +3

As software usage continues to expand, package managers automatically resolve dependencies to construct a dependency graph based on user-specified requirements. These explicitly de…

cs.CR2026

Mutate to Bypass: Autonomous Endpoint Evasion via Knowledge-Driven Multi-Agent Orchestration

Weifeng Yuan, Wenbo Guo, Qingyun Du +4

Public reports and open-source resources expose many EDR evasion techniques, but it remains unclear whether commercial Endpoint Detection and Response (EDR) systems can withstand t…

cs.SE2026

Skills That Don't Exist: A Large-Scale Study of Hallucinated Skill Recommendation in LLM Agents

Weifeng Yuan, Wenbo Guo, Feng Dong +2

LLM agents acquire new capabilities by downloading skills from open registries. Instead of browsing these catalogs manually, developers typically ask the agent to recommend and ins…

cs.CR2026

MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skills

Wenbo Guo, Wei Zeng, Chengwei Liu +5

AI coding agents such as Claude Code and Gemini CLI increasingly extend themselves with third-party skills: markdown packages bundling natural-language instructions, executable scr…

cs.SE2026

How Effective Are NPM Malicious Package Detectors? A Large-Scale Empirical Study

Wenbo Guo, Zhongwen Chen, Zhengzi Xu +7

The NPM ecosystem faces escalating threats from malicious packages that exploit its open publication model. While numerous detection tools have been proposed, they are evaluated on…

cs.SE2026

Latent Reuse in Agent Skills: Multi-modal Clone Detection at Ecosystem Scale

Jiaying Zhu, Lyuye Zhang, Wenbo Guo +1

An agent skill is a reusable package for extending an LLM agent, typically a SKILL.md file that combines YAML metadata, natural-language instructions, and executable code. Public r…

cs.CR2026

Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework

Wenbo Guo, Chengwei Liu, Ming Kang +5

The Python Package Index (PyPI) has become a target for malicious actors, yet existing detection tools generate false positive rates of 15-30%, incorrectly flagging one-third of le…

cs.SE2026★ 1 cited

Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages

Wenbo Guo, Shiwen Song, Jiaxun Guo +5

Open-source ecosystems such as NPM and PyPI are increasingly targeted by supply chain attacks, yet existing detection methods either depend on fragile handcrafted rules or data-dri…