| Title | Mystique: Uncovering Information Leakage from Browser Extensions |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Chen, Quan, Kapravelos, Alexandros |
| Conference Name | Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5693-0 |
| Keywords | browser extensions, Human Behavior, information flow, insider threat, JavaScript, Metrics, privacy, pubcrawl, resilience, taint analysis |
| Abstract | Browser extensions are small JavaScript, CSS and HTML programs that run inside the browser with special privileges. These programs, often written by third parties, operate on the pages that the browser is visiting, giving the user a programmatic way to configure the browser. The privacy implications that arise by allowing privileged third-party code to execute inside the users' browser are not well understood. In this paper, we develop a taint analysis framework for browser extensions and use it to perform a large scale study of extensions in regard to their privacy practices. We first present a hybrid approach to traditional taint analysis: by leveraging the fact that extension source code is available to the runtime JavaScript engine, we implement as well as enhance traditional taint analysis using information gathered from static data flow and control-flow analysis of the JavaScript source code. Based on this, we further modify the Chromium browser to support taint tracking for extensions. We analyzed 178,893 extensions crawled from the Chrome Web Store between September 2016 and March 2018, as well as a separate set of all available extensions (2,790 in total) for the Opera browser at the time of analysis. From these, our analysis flagged 3,868 (2.13%) extensions as potentially leaking privacy-sensitive information. The top 10 most popular Chrome extensions that we confirmed to be leaking privacy-sensitive information have more than 60 million users combined. We ran the analysis on a local Kubernetes cluster and were able to finish within a month, demonstrating the feasibility of our approach for large-scale analysis of browser extensions. At the same time, our results emphasize the threat browser extensions pose to user privacy, and the need for countermeasures to safeguard against misbehaving extensions that abuse their privileges. |
| URL | http://doi.acm.org/10.1145/3243734.3243823 |
| DOI | 10.1145/3243734.3243823 |
| Citation Key | chen_mystique:_2018 |
Mystique: Uncovering Information Leakage from Browser Extensions