Tracking Without Borders: Studying the Role of WebViews in Bridging Mobile and Web Tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
WebViews are a core component of today's in-app browsing technologies on mobile platforms, playing a central role in rendering web content like mobile advertisements. However, their use and potential to bridge web and mobile tracking paradigms comes at a significant privacy cost for users. Although prior work has highlighted privacy risks associated with WebViews, the real-world scale and privacy impact of their misuse and abuse remain unexplored due to the hybrid nature of WebViews-combining Java, native, and dynamically-loaded JavaScript (JS) code. In this paper, we present the first large-scale empirical study of WebView abuse in Android apps. We analyze how app developers and third-party SDKs facilitate user tracking by configuring WebViews to bypass default platform privacy protections and enable invasive tracking through JavaScript code. Using a novel analysis pipeline that combines static and dynamic analysis of Java/Kotlin code and JavaScript, we reveal how numerous actors undermine users' privacy and exploit WebViews in the wild. We show that harmful JavaScript code, often distributed via unvetted Real-Time Bidding (RTB) processes, exploits WebViews to perform advanced tracking techniques such as cookie sync-ing, canvas fingerprinting, and misuse of the Java-JS interface and permission-protected JavaScript APIs to silently leak unique user identifiers and geolocation data without user awareness for cross-platform tracking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it