Fuzzing Technologies

• Introduction
• Fuzzing Book
• The Art, Science, and Engineering of Fuzzing: A Survey IEEE Trans. on Software Engineering 2019
  Authors: Valentin J.M. Manes, HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J. Schwartz, and Maverick Woo
• Fuzzing: A Survey for Roadmap ACM Computing Surveys 2022
  Authors: Xiaogang Zhu, Sheng Wen, Seyit Camtepe, Yang Xiang
• Emerging Trends
• Accelerating Fuzzing through Prefix-Guided Execution OOPSLA 2023
  Authors: SHAOHUA LI and ZHENDONG SU
• DAFL: Directed Grey-box Fuzzing guided by Data Dependency USENIX 2023
  Authors: Tae Eun Kim, Jaeseung Choi, Kihong Heo, Sang Kil Cha
• Qsym : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing USENIX 2018
  Authors: Insu Yun, Sangho Lee, Meng Xu, Yeongjin Jang, and ; Taesoo Kim
• Fuzzle: Making a Puzzle for Fuzzers ASE 2022
  Authors: Haeun Lee, Soomin Kim, Sang Kil Cha
• AFL++ : Combining Incremental Steps of Fuzzing Research 14th USENIX Workshop on Offensive Technologies (WOOT 20)
  Authors: Andrea Fioraldi and Dominik Maier and Heiko Ei{\ss}feldt and Marc Heuse
• NEUZZ: Efficient Fuzzing with Neural Program Smoothing 40th IEEE Symposium on Security and Privacy, 2019
  Authors: Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana
• Revisiting Neural Program Smoothing for Fuzzing The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2023
  Authors: Maria-Irina Nicolae, Max Eisele, Andreas Zeller
• Tools
• AFL++
• OSS-Fuzz
• QSYM
• NEUZZ: a neural-network assisted fuzzer
• MLFuzz: fuzzing with machine learning