mlsanityData to 14 Oct 2026

When Fuzzing Meets LLMs: Challenges and Opportunities

arXiv 2404.16297 ↗First posted 25 Apr 2024cs.SE cs.AIcited by 8

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Abstract (arXiv)

Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs). Despite their potential, LLMs face specific challenges in fuzzing. In this paper, we identified five major challenges of LLM-assisted fuzzing. To support our findings, we revisited the most recent papers from top-tier conferences, confirming that these challenges are widespread. As a remedy, we propose some actionable recommendations to help improve applying LLM in Fuzzing and conduct preliminary evaluations on DBMS fuzzing. The results demonstrate that our recommendations effectively address the identified challenges.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

11 of 41 references.

References · 11

  1. 2018Object Hallucination in Image Captioningcited by 669
  2. 2023ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understandingcited by 176
  3. 2022Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Modelscited by 103
  4. 2023Effective Test Generation Using Pre-trained Large Language Models and Mutation Testingcited by 51
  5. 2023Large Language Model-Aware In-Context Learning for Code Generationcited by 43
  6. 2023WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Modelscited by 37
  7. 2023Prompt Fuzzing for Fuzz Driver Generationcited by 27
  8. 2023Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Promptingcited by 23
  9. 2023How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generationcited by 21
  10. 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
  11. 2021Industry Practice of Coverage-Guided Enterprise-Level DBMS Fuzzingcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 71
  2. 2025Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overviewcited by 7
  3. 2024Detecting Buggy Contracts via Smart Testingcited by 3
  4. 2025deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnessescited by 1
  5. 2025LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Modelscited by 0
  6. 2026CHASE: LLM Agents for Dissecting Malicious PyPI Packagescited by 0
  7. 2026Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzingcited by 0
  8. 2026From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practicecited by 0