mlsanityData to 14 Oct 2026

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

arXiv 1812.00140 ↗First posted 1 Dec 2018cs.CR cs.SEcited by 62

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 4; 2024: 7; 2025: 15; 2026: 25.

62 papers.

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  1. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 155
  2. 2023FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Modelscited by 51
  3. 2022NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilerscited by 30
  4. 2026ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?cited by 29
  5. 2025AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunitiescited by 28
  6. 2024Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Modelscited by 14
  7. 2025Rethinking Verification for LLM Code Generation: From Generation to Testingcited by 14
  8. 2022Search-Based Testing of Reinforcement Learningcited by 13
  9. 2024VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Modelscited by 11
  10. 2022Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutationcited by 10
  11. 2024Multi-Programming Language Sandbox for LLMscited by 10
  12. 2024VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patchingcited by 9
38 more
  1. 2021Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systemscited by 8
  2. 2024LProtector: An LLM-driven Vulnerability Detection Systemcited by 8
  3. 2023Exploring the Limits of ChatGPT in Software Security Applicationscited by 6
  4. 2025Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspectivecited by 6
  5. 2021On managing vulnerabilities in AI/ML systemscited by 5
  6. 2023BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzercited by 5
  7. 2021Towards Making Deep Learning-based Vulnerability Detectors Robustcited by 4
  8. 2024On the Challenges of Fuzzing Techniques via Large Language Modelscited by 4
  9. 2021State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzingcited by 3
  10. 2025LibLMFuzz: LLM-Augmented Fuzz Target Generation for Black-box Librariescited by 3
  11. 2026Large Language Models Hack Rewards, and Societycited by 3
  12. 2021Literature review on vulnerability detection using NLP technologycited by 2
  13. 2022Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniquescited by 2
  14. 2024FuzzCoder: Byte-level Fuzzing Test via Large Language Modelcited by 2
  15. 2025FalseCrashReducer: Mitigating False Positive Crashes in OSS-Fuzz-Gen Using Agentic AIcited by 2
  16. 2026VulnResolver: A Hybrid Agent Framework for LLM-Based Automated Vulnerability Issue Resolutioncited by 2
  17. 2026Evaluating LLM-Based Test Generation Under Software Evolutioncited by 2
  18. 2026SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplacescited by 2
  19. 2025Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challengescited by 1
  20. 2025SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzingcited by 1
  21. 2026Just-in-Time Catching Test Generation at Metacited by 1
  22. 2026NAAMSE: Framework for Evolutionary Security Evaluation of Agentscited by 1
  23. 2026KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Modelscited by 1
  24. 2026The Greatness of Science Cannot Be Planned: Agentic Auto-Research is Fuzz Testingcited by 1
  25. 2022Learning Failure-Inducing Models for Testing Software-Defined Networkscited by 0
  26. 2022Quality-diversity in dissimilarity spacescited by 0
  27. 2025Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Aheadcited by 0
  28. 2025Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Explorationcited by 0
  29. 2025LLMs and Fuzzing in Tandem: A New Approach to Automatically Generating Weakest Preconditionscited by 0
  30. 2025MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzingcited by 0
  31. 2025LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Modelscited by 0
  32. 2025Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.jscited by 0
  33. 2025On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detectioncited by 0
  34. 2025From Coverage to Causes: Data-Centric Fuzzing for JavaScript Enginescited by 0
  35. 2026Testing Storage-System Correctness: Challenges, Fuzzing Limitations, and AI-Augmented Opportunitiescited by 0
  36. 2026AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applicationscited by 0
  37. 2026Fuzzing with Agents? Generators Are All You Needcited by 0
  38. 2026A Multi-Agent Framework for Automated Exploit Generation with Constraint-Guided Comprehension and Reflectioncited by 0

50 most cited of 62 All 62 (JSON)