Practical Automated Detection of Malicious npm Packages

Abstract

The npm registry is one of the pillars of the JavaScript and TypeScript ecosystems, hosting over 1.7 million packages ranging from simple utility libraries to complex frameworks and entire applications. Due to the overwhelming popularity of npm, it has become a prime target for malicious actors, who publish new packages or compromise existing packages to introduce malware that tampers with or exfiltrates sensitive data from users who install either these packages or any package that (transitively) depends on them. Defending against such attacks is essential to maintaining the integrity of the software supply chain, but the sheer volume of package updates makes comprehensive manual review infeasible. We present Amalfi, a machine-learning based approach for automatically detecting potentially malicious packages comprised of three complementary techniques. We start with classifiers trained on known examples of malicious and benign packages. If a package is flagged as malicious by a classifier, we then check whether it includes metadata about its source repository, and if so whether the package can be reproduced from its source code. Packages that are reproducible from source are not usually malicious, so this step allows us to weed out false positives. Finally, we also employ a simple textual clone-detection technique to identify copies of malicious packages that may have been missed by the classifiers, reducing the number of false negatives. Amalfi improves on the state of the art in that it is lightweight, requiring only a few seconds per package to extract features and run the classifiers, and gives good results in practice: running it on 96287 package versions published over the course of one week, we were able to identify 95 previously unknown malware samples, with a manageable number of false positives.

arXiv comment12 pages, accepted for publication at ICSE 2022

References in corpus (3)

• Small World with High Risks: A Study of Security Threats in the npm Ecosystem
• A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI
• I Know What You Imported Last Summer: A study of security threats in thePython ecosystem

Cited by in corpus (11)

• BOMs Away! Inside the Minds of Stakeholders: A Comprehensive Study of Bills of Materials for Software Systems
• Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
• A Benchmark Comparison of Python Malware Detection Approaches
• A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks
• Towards Robust Detection of Open Source Software Supply Chain Poisoning Attacks in Industry Environments
• Malicious Package Detection using Metadata Information
• The Hitchhiker's Guide to Malicious Third-Party Dependencies
• Pinning Is Futile: You Need More Than Local Dependency Versioning to Defend against Supply Chain Attacks
• "I see models being a whole other thing": An Empirical Study of Pre-Trained Model Naming Conventions and A Tool for Enhancing Naming Consistency
• Trusting code in the wild: Exploring contributor reputation measures to review dependencies in the Rust ecosystem
• SoK: Towards Reproducibility for Software Packages in Scripting Language Ecosystems