Survey on Fuzzing Test in Deep Learning Frameworks
With the widespread application of deep learning technology in various fields,ensuring the security and stability of its frameworks has become crucial.This paper starts from the user's perspective to analyze the types of vulnerabilities that different user groups may encounter and the corresponding fuzzing test methods.The article first introduced the development background and importance of deep learning frameworks,then discussed in detail the current state of testing research for model libraries,deep learning frameworks,and compilers,and reviewed key techniques such as model mutation,weight generation,sample construction,and model testing.Then the article analyzed the root cause of bug in PyTorch and MLIR.Finally,the article looked forward to future research directions,including error localization and automatic repair techniques,as well as fuzzing test enhanced by large language models.
deep learningfuzzing testtest case generationmachine learning
万方数据
维普
