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.

翻译：GPU内存错误是深度学习（DL）框架面临的关键威胁，可能导致系统崩溃甚至引发安全问题。本文提出GPU-Fuzz，该模糊测试工具通过将算子参数建模为形式化约束，能够高效定位此类问题。GPU-Fuzz利用约束求解器生成测试用例，系统性地探测GPU内核中易出错的边界条件。在PyTorch、TensorFlow和PaddlePaddle框架上的应用结果表明，我们发现了13个未知漏洞，验证了GPU-Fuzz在检测内存错误方面的有效性。