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.

Pressured by existing regulations such as the EU GDPR, online services must advertise a personal data protection policy declaring the types and purposes of collected personal data, which must then be strictly enforced as per the consent decisions made by the users. However, due to the lack of system-level support, obtaining strong guarantees of policy enforcement is hard, leaving the door open for software bugs and vulnerabilities to cause GDPR-compliance violations.We present RuleKeeper, a GDPR-aware personal data policy compliance system for web development frameworks. Currently ported for the MERN framework, RuleKeeper allows web developers to specify a GDPR manifest from which the data protection policy of the web application is automatically generated and is transparently enforced through static code analysis and runtime access control mechanisms. GDPR compliance is checked in a cross-cutting manner requiring few changes to the application code. We used our prototype implementation to evaluate RuleKeeper with four real-world applications. Our system can model realistic GDPR data protection requirements, adds modest performance overheads to the web application, and can detect GDPR violation bugs.

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 innpmadvisory 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.

Pressured by existing regulations such as the EU GDPR, online services must advertise a personal data protection policy declaring the types and purposes of collected personal data, which must then be strictly enforced as per the consent decisions made by the users. However, due to the lack of system-level support, obtaining strong guarantees of policy enforcement is hard, leaving the door open for software bugs and vulnerabilities to cause GDPR-compliance violations. We present ongoing work on building a GDPR-aware personal data policy compliance system for web development frameworks. Currently prototyped for the MERN framework, our system allows web developers to specify a GDPR manifest from which the data protection policy of the web application is automatically generated and is transparently enforced through static code analysis and runtime access control mechanisms. GDPR compliance is checked in a cross-cutting manner requiring few changes to the application code. We evaluate our prototype with four real-world applications. Our system can model realistic GDPR data protection requirements, adds modest performance overheads to the web application, and can detect GDPR violation bugs.

WebAssembly is a new binary instruction format that allows targeted compiled code written in high-level languages to be executed with near-native speed by the browser’s JavaScript engine. However, given that WebAssembly binaries can be compiled from unsafe languages like C/C++, classical code vulnerabilities such as buffer overflows or format strings can be transferred over from the original programs down to the cross-compiled binaries. As a result, this possibility of incorporating vulnerabilities in WebAssembly modules has widened the attack surface of modern web applications. This paper presents Wasmati, a static analysis tool for finding security vulnerabilities in WebAssembly binaries. It is based on the generation of a code property graph (CPG), a program representation previously adopted for detecting vulnerabilities in various languages but hitherto unapplied to WebAssembly. We formalize the definition of CPG for WebAssembly, introduce techniques to generate CPG for complex WebAssembly, and present four different query specification languages for finding vulnerabilities by traversing a program’s CPG. We implemented ten queries capturing different vulnerability types and extensively tested Wasmati on four heterogeneous datasets. We show that Wasmati can scale the generation of CPGs for large real-world applications and can efficiently find vulnerabilities for all our query types. We have also tested our tool on WebAssembly binaries collected in the wild and identified several potential vulnerabilities, some of which we have manually confirmed to exist unless the enclosing application properly sanitizes the interaction with such affected binaries.