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IDFuzz: Intelligent Directed Grey-box Fuzzing

on YouTube 11m
Talkat USENIX Security '25 · 2025-08-13..08-15· [security, software-architecture]

speakers

name:Yiyang Chen
links:none on file
name:Chao Zhang
links:none on file
name:Long Wang
links:none on file
name:Wenyu Zhu
links:none on file
name:Changhua Luo
links:none on file
name:Nuoqi Gui
links:none on file
name:Zheyu Ma
links:none on file
name:Xingjian Zhang
links:none on file
name:Bingkai Su
links:none on file

resources

video:https://youtu.be/uCl0MvchI08
slides:none on file

summary

IDFuzz uses a neural network to learn which input offsets matter and then mutates those fields, claiming a 2.48x median speedup on the Google Fuzzer Test Suite and 91.86% fewer ineffective mutations; it finds six new vulnerabilities and yields four CVE IDs so far. Key techniques are branch encoding to represent sibling branches, adaptive data set generation to train compact models, and gradient filtering plus DB‑scan clustering to turn gradients into practical field mutations.

AI-written summary · about

chapters (10)

  1. 0:00IDFuzz and directive fuzzing
  2. 1:13Limitations of directive fuzzing
  3. 2:16Mutation strategy challenges
  4. 3:00Neural network model
  5. 4:04JPEG parsing vulnerability
  6. 5:49Gradient-guided mutation
  7. 6:55IDFuzz innovations
  8. 8:21Evaluation results
  9. 9:49Discovered vulnerabilities
  10. 10:11Conclusion

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