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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.10478 |
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| _version_ | 1866910035622232064 |
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| author | Li, Zihao Lu, Hongyi Guo, Yanan Zhang, Zhenkai Wang, Shuai Zhang, Fengwei |
| author_facet | Li, Zihao Lu, Hongyi Guo, Yanan Zhang, Zhenkai Wang, Shuai Zhang, Fengwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10478 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks Li, Zihao Lu, Hongyi Guo, Yanan Zhang, Zhenkai Wang, Shuai Zhang, Fengwei Cryptography and Security Machine Learning 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. |
| title | GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2602.10478 |