# The Art, Science, and Engineering of Fuzzing: A Survey

arXiv: https://arxiv.org/abs/1812.00140 · HTML: https://mlsanity.com/paper/1812.00140/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2018.

## Built on by (most cited first, 30 of 62)

- [Large Language Models for Code: Security Hardening and Adversarial Testing](https://mlsanity.com/paper/2302.05319/) (2023)
- [FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2309.05274/) (2023)
- [NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers](https://mlsanity.com/paper/2207.13066/) (2022)
- [ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?](https://mlsanity.com/paper/2605.11086/) (2026)
- [AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities](https://mlsanity.com/paper/2508.11126/) (2025)
- [Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2406.05892/) (2024)
- [Rethinking Verification for LLM Code Generation: From Generation to Testing](https://mlsanity.com/paper/2507.06920/) (2025)
- [Search-Based Testing of Reinforcement Learning](https://mlsanity.com/paper/2205.04887/) (2022)
- [VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models](https://mlsanity.com/paper/2406.07595/) (2024)
- [Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation](https://mlsanity.com/paper/2202.09947/) (2022)
- [Multi-Programming Language Sandbox for LLMs](https://mlsanity.com/paper/2410.23074/) (2024)
- [VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching](https://mlsanity.com/paper/2409.10756/) (2024)
- [Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2109.06404/) (2021)
- [LProtector: An LLM-driven Vulnerability Detection System](https://mlsanity.com/paper/2411.06493/) (2024)
- [Exploring the Limits of ChatGPT in Software Security Applications](https://mlsanity.com/paper/2312.05275/) (2023)
- [Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective](https://mlsanity.com/paper/2505.10494/) (2025)
- [On managing vulnerabilities in AI/ML systems](https://mlsanity.com/paper/2101.10865/) (2021)
- [BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer](https://mlsanity.com/paper/2305.12534/) (2023)
- [Towards Making Deep Learning-based Vulnerability Detectors Robust](https://mlsanity.com/paper/2108.00669/) (2021)
- [On the Challenges of Fuzzing Techniques via Large Language Models](https://mlsanity.com/paper/2402.00350/) (2024)
- [State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing](https://mlsanity.com/paper/2112.15498/) (2021)
- [LibLMFuzz: LLM-Augmented Fuzz Target Generation for Black-box Libraries](https://mlsanity.com/paper/2507.15058/) (2025)
- [Large Language Models Hack Rewards, and Society](https://mlsanity.com/paper/2606.04075/) (2026)
- [Literature review on vulnerability detection using NLP technology](https://mlsanity.com/paper/2104.11230/) (2021)
- [Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques](https://mlsanity.com/paper/2202.12139/) (2022)
- [FuzzCoder: Byte-level Fuzzing Test via Large Language Model](https://mlsanity.com/paper/2409.01944/) (2024)
- [FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AI](https://mlsanity.com/paper/2510.02185/) (2025)
- [VulnResolver: A Hybrid Agent Framework for LLM-Based Automated Vulnerability Issue Resolution](https://mlsanity.com/paper/2601.13933/) (2026)
- [Evaluating LLM-Based Test Generation Under Software Evolution](https://mlsanity.com/paper/2603.23443/) (2026)
- [SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces](https://mlsanity.com/paper/2607.02345/) (2026)

## Abstract (arXiv)

Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.