Machine Learning for Fuzzing

Technique

Machine Learning for Fuzzing refers to the use of machine-learning techniques to improve fuzz testing, including input mutation, coverage improvement, bypassing validation barriers, failure-inducing model learning, and other stages of the fuzzing process. The evidence describes it as an active research direction, with survey-level evidence of performance improvements over traditional fuzzing in some studies, an SDN-focused method called FuzzSDN, and a future-work direction for coverage-guided fuzzing of instruction set simulators.

First seen 5/28/2026

Last seen 5/29/2026

Evidence 3 chunks

Wiki v2

WIKI

Overview

Machine Learning for Fuzzing is the integration of machine-learning techniques into fuzz testing. A 2019 systematic review characterizes this line of work as a response to challenges in traditional fuzzing, including how to mutate seed inputs, increase code coverage, and bypass verification checks. The review reports that machine learning has been used across multiple stages of fuzzing and analyzes work in terms of algorithm selection, preprocessing, datasets, evaluation metrics, and hyperparameter settings.

Reported uses and benefits

READ FULL ARTICLE →

NEIGHBORHOOD

No graph connections found for this entity yet. It may appear in future ingestion runs.

explore full graph →

RELATIONSHIPS

1 connections

Verifying Instruction Set Simulators using Coverage-guided Fuzzing ← mentions 90% 1e

The paper mentions machine learning integration into fuzzing as a promising future direction.

LINKED ENTITIES

1 links

Verifying Instruction Set Simulators using Coverage-guided Fuzzing MENTIONS Extracted graph relationship

CITATIONS

8 sources

8 citations — click to expand

[1] Machine learning is introduced into fuzzing to address challenges such as seed mutation, increasing code coverage, and bypassing verification, and has been studied across multiple stages of the fuzzing process. A systematic review of fuzzing based on machine learning techniques

[2] The systematic review analyzes machine-learning-based fuzzing work by algorithm selection, preprocessing methods, datasets, evaluation metrics, and hyperparameter settings. A systematic review of fuzzing based on machine learning techniques

[3] The systematic review reports that machine learning can improve fuzzing performance, while also noting limitations such as unbalanced training samples and difficulty extracting vulnerability-related characteristics. A systematic review of fuzzing based on machine learning techniques

[4] FuzzSDN is a machine-learning-guided fuzzing method for SDN-based systems that aims to generate failure-inducing test data and learn failure-inducing models. Learning Failure-Inducing Models for Testing Software-Defined Networks

[5] FuzzSDN was evaluated on systems controlled by two open-source SDN controllers and was reported to generate at least 12 times more failures than state-of-the-art methods in one robust-controller setting, with learned models averaging 98% precision and 86% recall. Learning Failure-Inducing Models for Testing Software-Defined Networks

[6] In instruction set simulator verification, Herdt et al. implemented a coverage-guided fuzzing approach with extensions on top of libFuzzer and evaluated it on three publicly available RISC-V instruction set simulators. Verifying Instruction Set Simulators using Coverage-guided Fuzzing

[7] The instruction set simulator verification paper treats integrating machine-learning techniques into fuzzing as promising future work rather than as part of its implemented approach. Verifying Instruction Set Simulators using Coverage-guided Fuzzing

[8] The instruction set simulator verification paper cites Bayesian networks and other machine-learning techniques as prior approaches for improving random generation of processor-level stimuli. Verifying Instruction Set Simulators using Coverage-guided Fuzzing