Protect Your Secrets: Understanding and Measuring Data Exposure in VSCode Extensions
Yue Liu, Chakkrit Tantithamthavorn, Li Li

TL;DR
This paper systematically analyzes data exposure risks in VSCode extensions, revealing significant security flaws and proposing an automated framework to detect credential leaks in thousands of real-world extensions.
Contribution
It introduces a novel automated risk detection framework for VSCode extensions and provides the first large-scale analysis of data exposure vulnerabilities in this environment.
Findings
8.5% of analyzed extensions leak credential data
Identified multiple vectors for data exposure such as commands and user input
Provided security recommendations for extension developers
Abstract
Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance developer productivity. Especially, popular AI coding assistants like GitHub Copilot and Tabnine provide conveniences like automated code completion and debugging. While these extensions offer numerous benefits, they may introduce privacy and security concerns to software developers. However, there is no existing work that systematically analyzes the security and privacy concerns, including the risks of data exposure in VSCode extensions. In this paper, we investigate on the security issues of cross-extension interactions in VSCode and shed light on the vulnerabilities caused by data exposure among different extensions. Our study uncovers high-impact security flaws that could allow adversaries to stealthily…
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Taxonomy
TopicsScientific Computing and Data Management · Security and Verification in Computing · Cloud Data Security Solutions
