A Review of Machine Learning Applications in Fuzzing
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Abstract (arXiv)
Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2019.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.
4 papers.
- 2023BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzercited by 5
- 2025SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzingcited by 1
- 2022DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Softwarecited by 0
- 2023Revisiting Neural Program Smoothing for Fuzzingcited by 0
- 2025SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing16 Nov 2025
- 2023Revisiting Neural Program Smoothing for Fuzzing28 Sep 2023
- 2023BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer21 May 2023
- 2022DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software29 Jan 2022