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Cross-site scripting (XSS) vulnerabilities are the most frequently reported web application vulnerability. As com- plex JavaScript applications become more widespread, DOM (Document Object Model) XSS vulnerabilities—a type of XSS vulnerability where the vulnerability is located in client-side JavaScript, rather than server-side code—are becoming more common. As the first contribution of this work, we empirically assess the impact of DOM XSS on the web using a browser with taint tracking embedded in the JavaScript engine. Building on the methodology used in a previous study that crawled popular websites, we collect a current dataset of potential DOM XSS vulnerabilities. We improve on the methodology for confirming XSS vulnerabilities, and using this improved methodology, we find 83% more vulnerabilities than previous methodology applied to the same dataset. As a second contribution, we identify the causes of and discuss how to prevent DOM XSS vulnerabilities. One example of our findings is that custom HTML templating designs—a design pattern that could prevent DOM XSS vulnerabilities analogous to parameterized SQL—can be buggy in practice, allowing DOM XSS attacks. As our third contribution, we evaluate the error rates of three static-analysis tools to detect DOM XSS vulnerabilities found with dynamic analysis techniques using in-the-wild examples. We find static-analysis tools to miss 90% of bugs found by our dynamic analysis, though some tools can have very few false positives and at the same time find vulnerabilities not found using the dynamic analysis.
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UntrustIDE: Exploiting Weaknesses in VS Code Extensions
With the rise in threats against the software supply chain, developer integrated development environments (IDEs) present an attractive target for attackers. For example, researchers have found extensions for Visual Studio Code (VS Code) that start web servers and can be exploited via JavaScript executing in a web browser on the developer's host. This paper seeks to systematically understand the landscape of vulnerabilities in VS Code's extension marketplace. We identify a set of four sources of untrusted input and three code targets that can be used for code injection and file integrity attacks and use them to design taint analysis rules in CodeQL. We then perform an ecosystem-level analysis of the VS Code extension marketplace, studying 25,402 extensions that contain code. Our results show that while vulnerabilities are not pervasive, they exist and impact millions of users. Specifically, we find 21 extensions with verified proof of concept exploits of code injection attacks impacting a total of over 6 million installations. Through this study, we demonstrate the need for greater attention to the security of IDE extensions.
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- Award ID(s):
- 2207008
- PAR ID:
- 10516041
- Publisher / Repository:
- Internet Society
- Date Published:
- Journal Name:
- Proceedings of the ISOC Network and Distributed Systems Symposium (NDSS)
- Format(s):
- Medium: X
- Location:
- San Diego, CA, USA
- Sponsoring Org:
- National Science Foundation
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