GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks

• URL: http://arxiv.org/abs/2602.10478v2
• Date: Fri, 13 Feb 2026 03:04:02 GMT
• Title: GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks
• Abstract summary: GPU memory errors are a critical threat to deep learning (DL) frameworks, leading to crashes or even security issues.<n>We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints.
• Score: 16.88852734850369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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