Moneta: Ex-Vivo GPU Driver Fuzzing by Recalling In-Vivo Execution States

—Graphics Processing Units (GPUs) have become an indispensable part of modern computing infrastructure. They can execute massively parallel tasks on large data sets and have rich user space-accessible APIs for 3D rendering and general-purpose parallel programming. Unfortunately, the GPU drivers that bridge the gap between these APIs and the underlying hardware have grown increasingly large and complex over the years. Many GPU drivers now expose broad attack surfaces and pose serious security risks. Fuzzing is a proven automated testing method that mitigates these risks by identifying potential vulnerabilities. However, when applied to GPU drivers, existing fuzzers incur high costs and scale poorly because they rely on physical GPUs. Furthermore, they achieve limited effectiveness because they often fail to meet dependency and timing constraints while generating and executing input events. We present Moneta, a new ex-vivo approach to driver fuzzing that can statefully and effectively fuzz GPU drivers at scale. The key idea is (i) to recall past, in-vivo GPU driver execution states by synergistically combining snapshot-and-rehost and record-and-replay along with our proposed suite of GPU stack virtualization and introspection techniques, and (ii) to start parallel and stateful ex-vivo GPU driver fuzzing from the recalled states. We implemented a prototype of Moneta and evaluated it on three mainstream GPU drivers. Our prototype triggered deep, live GPU driver states during fuzzing, and found five previously unknown bugs in the NVIDIA GPU driver, three in the AMD Radeon GPU driver, and two in the ARM Mali GPU driver. These ten bugs were all confirmed by the respective vendors in response to our responsible disclosure, and five