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arXiv · 2602.10478

GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks

Zihao Li·Hongyi Lu·Yanan Guo·Zhenkai Zhang·Shuai Wang·Fengwei Zhang
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Abstract

GPU memory errors are a critical threat to deep learning (DL) frameworks, leading to crashes or even security issues. We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints. GPU-Fuzz utilizes a constraint solver to generate test cases that systematically probe error-prone boundary conditions in GPU kernels. Applied to PyTorch, TensorFlow, and PaddlePaddle, we uncovered 13 unknown bugs, demonstrating the effectiveness of GPU-Fuzz in finding memory errors.

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Zihao Li, Hongyi Lu, Yanan Guo, Zhenkai Zhang, Shuai Wang, Fengwei Zhang. 2026-02-11. GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks. https://arxiv.org/abs/2602.10478

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