Learn&Fuzz: Machine learning for input fuzzing

@article{Godefroid2017LearnFuzzML,
  title={Learn&Fuzz: Machine learning for input fuzzing},
  author={Patrice Godefroid and Hila Peleg and Rishabh Singh},
  journal={2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)},
  year={2017},
  pages={50-59}
}
Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF… CONTINUE READING

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