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Microarchitectural attacks on CPU structures have been studied in nativeapplications, as well as in web browsers. These attacks continue to be asubstantial threat to computing systems at all scales.  With the proliferation of heterogeneous systems and integration of hardwareaccelerators in every computing system, modern web browsers provide the supportof GPU-based acceleration for the graphics and rendering processes. Emergingweb standards also support the GPU acceleration of general-purpose computationwithin web browsers.  In this paper, we present a new attack vector for microarchitectural attacksin web browsers. We use emerging GPU accelerating APIs in modern browsers(specifically WebGPU) to launch a GPU-based cache side channel attack on thecompute stack of the GPU that spies on victim activities on the graphics(rendering) stack of the GPU. Unlike prior works that rely on JavaScript APIsor software interfaces to build timing primitives, we build the timer using GPUhardware resources and develop a cache side channel attack on Intel'sintegrated GPUs. We leverage the GPU's inherent parallelism at different levelsto develop high-resolution parallel attacks. We demonstrate that GPU-basedcache attacks can achieve a precision of 90 for website fingerprinting of 100top websites. We also discuss potential countermeasures against the proposedattack to secure the systems at a critical time when these web standards arebeing developed and before they are widely deployed.

WebGPU-SPY: Finding Fingerprints in the Sandbox through GPU Cache Attacks