Efficient Static Vulnerability Analysis for JavaScript with Multiversion Dependency Graphs

Author:

Ferreira Mafalda1ORCID,Monteiro Miguel1ORCID,Brito Tiago1ORCID,Coimbra Miguel E.1ORCID,Santos Nuno1ORCID,Jia Limin2ORCID,Santos José Fragoso1ORCID

Affiliation:

1. INESC-ID, Lisboa, Portugal / Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

2. Carnegie Mellon University, Pittsburgh, USA

Abstract

While static analysis tools that rely on Code Property Graphs (CPGs) to detect security vulnerabilities have proven effective, deciding how much information to include in the graphs remains a challenge. Including less information can lead to a more scalable analysis but at the cost of reduced effectiveness in identifying vulnerability patterns, potentially resulting in classification errors. Conversely, more information in the graph allows for a more effective analysis but may affect scalability. For example, scalability issues have been recently highlighted in ODGen, the state-of-the-art CPG-based tool for detecting Node.js vulnerabilities. This paper examines a new point in the design space of CPGs for JavaScript vulnerability detection. We introduce the Multiversion Dependency Graph (MDG), a novel graph-based data structure that captures the state evolution of objects and their properties during program execution. Compared to the graphs used by ODGen, MDGs are significantly simpler without losing key information needed for vulnerability detection. We implemented Graph.js, a new MDG-based static vulnerability scanner specialized in analyzing npm packages and detecting taint-style and prototype pollution vulnerabilities. Our evaluation shows that Graph.js outperforms ODGen by significantly reducing both the false negatives and the analysis time. Additionally, we have identified 49 previously undiscovered vulnerabilities in npm packages.

Publisher

Association for Computing Machinery (ACM)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An advanced computing approach for software vulnerability detection;Multimedia Tools and Applications;2024-06-27

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