Cover Image

Automatically Generating Rules of Malicious Software Packages via Large Language Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, XiangRui, Chen, HaoYu, He, Yongzhong, Niu, Wenjia, Li, Qiang
Format:	Preprint
Published:	2025
Subjects:	Software Engineering Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Analysis of Malicious Packages in Open-Source Software in the Wild
by: Zhou, Xiaoyan, et al.
Published: (2024)

A Large-scale Fine-grained Analysis of Packages in Open-Source Software Ecosystems
by: Zhou, Xiaoyan, et al.
Published: (2024)

Implicit Patterns in LLM-Based Binary Analysis
by: Li, Qiang, et al.
Published: (2026)

Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models
by: Zhang, Ying, et al.
Published: (2024)

CHASE: LLM Agents for Dissecting Malicious PyPI Packages
by: Toda, Takaaki, et al.
Published: (2026)

Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
by: Zhang, Junan, et al.
Published: (2023)

Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework
by: Guo, Wenbo, et al.
Published: (2026)

Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages
by: Guo, Wenbo, et al.
Published: (2026)

SoK: Towards Reproducibility for Software Packages in Scripting Language Ecosystems
by: Pohl, Timo, et al.
Published: (2025)

The Popularity Hypothesis in Software Security: A Large-Scale Replication with PHP Packages
by: Ruohonen, Jukka, et al.
Published: (2025)

Mind the Gap: Evaluating LLMs for High-Level Malicious Package Detection vs. Fine-Grained Indicator Identification
by: Ryan, Ahmed, et al.
Published: (2026)

DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem
by: Mehedi, Sk Tanzir, et al.
Published: (2025)

Accelerating Automatic Program Repair with Dual Retrieval-Augmented Fine-Tuning and Patch Generation on Large Language Models
by: Guo, Hanyang, et al.
Published: (2025)

DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection
by: Yang, Yanjing, et al.
Published: (2024)

Securing the Software Package Supply Chain for Critical Systems
by: Murali, Ritwik, et al.
Published: (2025)

"Elementary, My Dear Watson." Detecting Malicious Skills via Neuro-Symbolic Reasoning across Heterogeneous Artifacts
by: Wang, Shenao, et al.
Published: (2026)

Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models
by: Huang, Youwei, et al.
Published: (2025)

AgentGuard: A Multi-Agent Framework for Robust Package Confusion Detection via Hybrid Search and Metadata-Content Fusion
by: Li, Yu, et al.
Published: (2026)

AutoFirm: Automatically Identifying Reused Libraries inside IoT Firmware at Large-Scale
by: Chen, YongLe, et al.
Published: (2024)

APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching
by: Nong, Yu, et al.
Published: (2024)

MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code
by: Ning, Kaiwen, et al.
Published: (2024)

Maven-Hijack: Software Supply Chain Attack Exploiting Packaging Order
by: Reyes, Frank, et al.
Published: (2024)

Malicious ML Model Detection by Learning Dynamic Behaviors
by: Nambiar, Sarang, et al.
Published: (2026)

Signing in Four Public Software Package Registries: Quantity, Quality, and Influencing Factors
by: Schorlemmer, Taylor R, et al.
Published: (2024)

Understanding the Supply Chain and Risks of Large Language Model Applications
by: Ma, Yujie, et al.
Published: (2025)

Automatic Selection of Protections to Mitigate Risks Against Software Applications
by: Canavese, Daniele, et al.
Published: (2025)

MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
by: Sun, Tiezhu, et al.
Published: (2025)

Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs
by: Zhao, Jian, et al.
Published: (2024)

Unknown Attack Detection in IoT Networks using Large Language Models: A Robust, Data-efficient Approach
by: Ali, Shan, et al.
Published: (2026)

Harnessing Large Language Models for Seed Generation in Greybox Fuzzing
by: Shi, Wenxuan, et al.
Published: (2024)

OmniBOR: A System for Automatic, Verifiable Artifact Resolution across Software Supply Chains
by: Seshadri, Bharathi, et al.
Published: (2024)

PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages
by: Simsek, Deniz, et al.
Published: (2025)

Unveiling A Hidden Risk: Exposing Educational but Malicious Repositories in GitHub
by: Masud, Md Rayhanul, et al.
Published: (2024)

From Component Manipulation to System Compromise: Understanding and Detecting Malicious MCP Servers
by: Huang, Yiheng, et al.
Published: (2026)

Assessing the Software Security Comprehension of Large Language Models
by: Siddiq, Mohammed Latif, et al.
Published: (2025)

Patch2QL: Discover Cognate Defects in Open Source Software Supply Chain With Auto-generated Static Analysis Rules
by: Wang, Fuwei, et al.
Published: (2024)

Automatic Attack Script Generation: a MDA Approach
by: Goux, Quentin, et al.
Published: (2026)

Generating API Parameter Security Rules with LLM for API Misuse Detection
by: Liu, Jinghua, et al.
Published: (2024)

VulnRepairEval: An Exploit-Based Evaluation Framework for Assessing Large Language Model Vulnerability Repair Capabilities
by: Wang, Weizhe, et al.
Published: (2025)

LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing
by: Zhang, Hongxiang, et al.
Published: (2024)