# When Fuzzing Meets LLMs: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2404.16297 · HTML: https://mlsanity.com/paper/2404.16297/

## 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 (most cited first, 11 of 11)

- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding](https://mlsanity.com/paper/2305.14196/) (2023)
- [Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models](https://mlsanity.com/paper/2212.14834/) (2022)
- [Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing](https://mlsanity.com/paper/2308.16557/) (2023)
- [Large Language Model-Aware In-Context Learning for Code Generation](https://mlsanity.com/paper/2310.09748/) (2023)
- [WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models](https://mlsanity.com/paper/2310.15991/) (2023)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting](https://mlsanity.com/paper/2304.11686/) (2023)
- [How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation](https://mlsanity.com/paper/2307.12469/) (2023)
- [Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach](https://mlsanity.com/paper/2310.06680/) (2023)
- [Industry Practice of Coverage-Guided Enterprise-Level DBMS Fuzzing](https://mlsanity.com/paper/2103.00804/) (2021)

## Built on by (most cited first, 8 of 8)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview](https://mlsanity.com/paper/2503.10784/) (2025)
- [Detecting Buggy Contracts via Smart Testing](https://mlsanity.com/paper/2409.04597/) (2024)
- [deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses](https://mlsanity.com/paper/2506.15648/) (2025)
- [LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models](https://mlsanity.com/paper/2510.10179/) (2025)
- [CHASE: LLM Agents for Dissecting Malicious PyPI Packages](https://mlsanity.com/paper/2601.06838/) (2026)
- [Coverage-Guided Multi-Agent Harness Generation for Java Library Fuzzing](https://mlsanity.com/paper/2603.08616/) (2026)
- [From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practice](https://mlsanity.com/paper/2607.16660/) (2026)

## 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.