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