Technology Category
Machine Learning: Large Multimodal Models (LMMs)Multiagent Systems: Coordination and CollaborationNatural Language Processing: Safety and Robustness
Many hands make light work: An LLM-based multi-agent system for detecting malicious PyPI packages
🤖 AI Summary
This work proposes LAMPS, a multi-agent collaborative system based on large language models (LLMs) to detect malicious packages in the PyPI repository that exhibit strong semantic obfuscation and evade traditional rule-based detection methods. LAMPS employs a modular architecture coordinated via the CrewAI framework, integrating four specialized agents that synergistically combine fine-tuned CodeBERT for classification and LLaMA-3 for contextual reasoning. This approach enables high-precision, interpretable, and scalable supply chain security analysis. Evaluated on the D1 and D2 datasets, LAMPS achieves accuracy rates of 97.7% and 99.5%, respectively, significantly outperforming baseline methods including MPHunter, retrieval-augmented generation (RAG), and single-agent approaches, thereby overcoming the limitations of individual models in recognizing complex malicious behavioral patterns.
Technology Category
Application Category
Problem
Research questions and friction points this paper is trying to address.
malicious code detection
software supply chain security
PyPI packages
open-source repositories
adversarial components
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent system
large language models
malicious package detection
software supply chain security
modular LLM reasoning
🔎 Similar Papers
No similar papers found.
