# A Review of Machine Learning Applications in Fuzzing

arXiv: https://arxiv.org/abs/1906.11133 · HTML: https://mlsanity.com/paper/1906.11133/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer](https://mlsanity.com/paper/2305.12534/) (2023)
- [SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing](https://mlsanity.com/paper/2511.12448/) (2025)
- [DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software](https://mlsanity.com/paper/2201.12602/) (2022)
- [Revisiting Neural Program Smoothing for Fuzzing](https://mlsanity.com/paper/2309.16618/) (2023)

## 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.