Study of JavaScript Static Analysis Tools for Vulnerability Detection in Node.js Packages

With the emergence of the Node.js ecosystem, JavaScript has become a widely used programming language for implementing server-side web applications. In this article, we present the first empirical study of static code analysis tools for detecting vulnerabilities in Node.js code. To conduct a compreh...

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Bibliographic Details
Published in:IEEE transactions on reliability Vol. 72; no. 4; pp. 1324 - 1339
Main Authors: Brito, Tiago, Ferreira, Mafalda, Monteiro, Miguel, Lopes, Pedro, Barros, Miguel, Santos, Jose Fragoso, Santos, Nuno
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applications programs
Automatic testing
Codes
Computer security
Datasets
Ecosystems
Empirical analysis
Engines
Java
Nodes
Static analysis
Static code analysis
Task analysis
ISSN:0018-9529, 1558-1721
Online Access:Get full text
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Summary:With the emergence of the Node.js ecosystem, JavaScript has become a widely used programming language for implementing server-side web applications. In this article, we present the first empirical study of static code analysis tools for detecting vulnerabilities in Node.js code. To conduct a comprehensive tool evaluation, we created the largest known curated dataset of Node.js code vulnerabilities. We characterized and annotated a set of 957 vulnerabilities by analyzing information contained in npm advisory reports. We tested nine different tools and found that many important vulnerabilities appearing in the OWASP top-10 are not detected by any tool. The three best performing tools combined only detect up to 57.6% of all vulnerabilities in the dataset, but at a very low precision of 0.11%. Our curated dataset offers a new benchmark to help characterize existing Node.js code vulnerabilities and foster the development of better vulnerability detection tools for Node.js code.
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ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2023.3286301