What Browsers Do in the Shaders: A Measurement Study of WebGPU Privacy
arXiv SecurityArchived Jun 26, 2026✓ Full text saved
arXiv:2606.26412v1 Announce Type: new Abstract: WebGPU lets ordinary web pages run GPU workloads through a validated programming model. Validation protects memory safety, but shared browser, driver, OS, and GPU state can still expose privacy-relevant signals. We present WGPULens, a framework for measuring those signals across controlled scenarios, browser-native co-residency, a participant field study, public page loads, and mitigation policies. Our framework separates measurements: controlled s
Full text archived locally
✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 24 Jun 2026]
What Browsers Do in the Shaders: A Measurement Study of WebGPU Privacy
Igor Santos-Grueiro
WebGPU lets ordinary web pages run GPU workloads through a validated programming model. Validation protects memory safety, but shared browser, driver, OS, and GPU state can still expose privacy-relevant signals. We present WGPULens, a framework for measuring those signals across controlled scenarios, browser-native co-residency, a participant field study, public page loads, and mitigation policies. Our framework separates measurements: controlled scenarios support leakage, boundary, and mitigation claims; participant runs support deployment, compatibility, and fingerprintability; and a Tranco crawl measures WebGPU exposure in real-world pages.
Our controlled results identify persistent pipeline compilation state as the clearest surface. Cold/warm pipeline probes reveal prior compilation state across selected origin, profile, and browser placements. Controlled browser/native experiments also show native GPU activity can be inferred from browser-visible observables under labeled workloads. Other resource probes provide weaker positive results and negative controls.
The participant field study shows active WebGPU behavior is highly distinctive within the sample, with deterministic components stable within runs and lower exact stability across repeated visits. A page-load crawl finds WebGPU use mainly as adapter probing and static support code, with no observed page-load shader, pipeline, queue, query, or map activity. Mitigation pilots identify source-level key separation as a proxy for evaluating pipeline-cache partitioning. Overall, WGPULens shows that WebGPU privacy analysis must be surface-specific: browsers need to measure which GPU state crosses which boundary, which browser-visible signals reveal it, and what the corresponding mitigations cost.
Comments: 20 pages
Subjects: Cryptography and Security (cs.CR)
Cite as: arXiv:2606.26412 [cs.CR]
(or arXiv:2606.26412v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.26412
Focus to learn more
Submission history
From: Igor Santos-Grueiro [view email]
[v1] Wed, 24 Jun 2026 22:11:06 UTC (42 KB)
Access Paper:
HTML (experimental)
view license
Current browse context:
cs.CR
< prev | next >
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Export BibTeX Citation
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Demos
Related Papers
About arXivLabs
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)