# Learn&Fuzz: Machine Learning for Input Fuzzing

arXiv: https://arxiv.org/abs/1701.07232 · HTML: https://mlsanity.com/paper/1701.07232/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2017.

## Built on by (most cited first, 23 of 23)

- [Fuzz4All: Universal Fuzzing with Large Language Models](https://mlsanity.com/paper/2308.04748/) (2023)
- [How Far Have We Gone in Vulnerability Detection Using Large Language Models](https://mlsanity.com/paper/2311.12420/) (2023)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [KernelGPT: Enhanced Kernel Fuzzing via Large Language Models](https://mlsanity.com/paper/2401.00563/) (2023)
- [LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors](https://mlsanity.com/paper/2308.13904/) (2023)
- [Deep-Learning-based Vulnerability Detection in Binary Executables](https://mlsanity.com/paper/2212.01254/) (2022)
- [Finding Missed Code Size Optimizations in Compilers using LLMs](https://mlsanity.com/paper/2501.00655/) (2024)
- [Learning Program Behavioral Models from Synthesized Input-Output Pairs](https://mlsanity.com/paper/2407.08597/) (2024)
- [Test2Vec: An Execution Trace Embedding for Test Case Prioritization](https://mlsanity.com/paper/2206.15428/) (2022)
- [Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems](https://mlsanity.com/paper/2502.11173/) (2025)
- [ARIST: An Effective API Argument Recommendation Approach](https://mlsanity.com/paper/2306.06620/) (2023)
- [FuzzCoder: Byte-level Fuzzing Test via Large Language Model](https://mlsanity.com/paper/2409.01944/) (2024)
- [Leveraging AI to optimize website structure discovery during Penetration Testing](https://mlsanity.com/paper/2101.07223/) (2021)
- [Reinforcement learning guided fuzz testing for a browser's HTML rendering engine](https://mlsanity.com/paper/2307.14556/) (2023)
- [SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing](https://mlsanity.com/paper/2511.12448/) (2025)
- [Bootstrapping Fuzzers for Compilers of Low-Resource Language Dialects Using Language Models](https://mlsanity.com/paper/2512.05887/) (2025)
- [Learning Failure-Inducing Models for Testing Software-Defined Networks](https://mlsanity.com/paper/2210.15469/) (2022)
- [Revisiting Neural Program Smoothing for Fuzzing](https://mlsanity.com/paper/2309.16618/) (2023)
- [An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detection](https://mlsanity.com/paper/2505.19059/) (2025)
- [DeepGo: Predictive Directed Greybox Fuzzing](https://mlsanity.com/paper/2507.21952/) (2025)
- [Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection](https://mlsanity.com/paper/2511.12164/) (2025)
- [ABTest: Behavior-Driven Testing for AI Coding Agents](https://mlsanity.com/paper/2604.03362/) (2026)
- [Automated Synthesis of Cloud Emulators](https://mlsanity.com/paper/2608.23842/) (2026)

## Abstract (arXiv)

Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser. We discuss (and measure) the tension between conflicting learning and fuzzing goals: learning wants to capture the structure of well-formed inputs, while fuzzing wants to break that structure in order to cover unexpected code paths and find bugs. We also present a new algorithm for this learn&fuzz challenge which uses a learnt input probability distribution to intelligently guide where to fuzz inputs.