Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols

Samuel Jero, Maria Leonor Pacheco, Dan Goldwasser, Cristina Nita-Rotaru

AAAI 2019 pp. 9478-9483

doi:10.1609/AAAI.V33I01.33019478 /aaai/2019/jero2019aaai-leveraging/

Abstract

Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.

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Cite

Text

Jero et al. "Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019478

Markdown

[Jero et al. "Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/jero2019aaai-leveraging/) doi:10.1609/AAAI.V33I01.33019478

BibTeX

@inproceedings{jero2019aaai-leveraging,
  title     = {{Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols}},
  author    = {Jero, Samuel and Pacheco, Maria Leonor and Goldwasser, Dan and Nita-Rotaru, Cristina},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {9478-9483},
  doi       = {10.1609/AAAI.V33I01.33019478},
  url       = {https://mlanthology.org/aaai/2019/jero2019aaai-leveraging/}
}