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Python Fuzzing for Trustworthy Machine Learning Frameworks
March 20, 2024, 4:11 a.m. | Ilya Yegorov, Eli Kobrin, Darya Parygina, Alexey Vishnyakov, Andrey Fedotov
cs.CR updates on arXiv.org arxiv.org
Abstract: Ensuring the security and reliability of machine learning frameworks is crucial for building trustworthy AI-based systems. Fuzzing, a popular technique in secure software development lifecycle (SSDLC), can be used to develop secure and robust software. Popular machine learning frameworks such as PyTorch and TensorFlow are complex and written in multiple programming languages including C/C++ and Python. We propose a dynamic analysis pipeline for Python projects using the Sydr-Fuzz toolset. Our pipeline includes fuzzing, corpus minimization, …
arxiv building can cs.ai cs.cr cs.se development frameworks fuzzing lifecycle machine machine learning popular python pytorch reliability secure software security software software development systems tensorflow trustworthy ai written
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