# Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead

arXiv: https://arxiv.org/abs/2503.00795 · HTML: https://mlsanity.com/paper/2503.00795/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions](https://mlsanity.com/paper/2308.09936/) (2023)
- [Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)](https://mlsanity.com/paper/2304.06815/) (2023)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [A Deep Dive into Large Language Models for Automated Bug Localization and Repair](https://mlsanity.com/paper/2404.11595/) (2024)
- [Prompt Fuzzing for Fuzz Driver Generation](https://mlsanity.com/paper/2312.17677/) (2023)
- [How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation](https://mlsanity.com/paper/2307.12469/) (2023)
- [Augmenting Greybox Fuzzing with Generative AI](https://mlsanity.com/paper/2306.06782/) (2023)
- [Fuzzing Deep-Learning Libraries via Automated Relational API Inference](https://mlsanity.com/paper/2207.05531/) (2022)
- [Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing](https://mlsanity.com/paper/2403.03897/) (2024)
- [Understanding Programs by Exploiting (Fuzzing) Test Cases](https://mlsanity.com/paper/2305.13592/) (2023)
- [Open Problems in Fuzzing RESTful APIs: A Comparison of Tools](https://mlsanity.com/paper/2205.05325/) (2022)
- [IntelliGen: Automatic Driver Synthesis for FuzzTesting](https://mlsanity.com/paper/2103.00862/) (2021)
- [BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing](https://mlsanity.com/paper/2202.13114/) (2022)
- [The Good, The Bad and The Missing: A Narrative Review of Cyber-security Implications for Australian Small Businesses](https://mlsanity.com/paper/2109.00733/) (2021)
- [ProphetFuzz: Fully Automated Prediction and Fuzzing of High-Risk Option Combinations with Only Documentation via Large Language Model](https://mlsanity.com/paper/2409.00922/) (2024)

## Abstract (arXiv)

Fuzz testing is a crucial component of software security assessment, yet its effectiveness heavily relies on valid fuzz drivers and diverse seed inputs. Recent advancements in Large Language Models (LLMs) offer transformative potential for automating fuzz testing (LLM4Fuzz), particularly in generating drivers and seeds. However, current LLM4Fuzz solutions face critical reliability challenges, including low driver validity rates and seed quality trade-offs, hindering their practical adoption. This paper aims to examine the reliability bottlenecks of LLM-driven fuzzing and explores potential research directions to address these limitations. It begins with an overview of the current development of LLM4SE and emphasizes the necessity for developing reliable LLM4Fuzz solutions. Following this, the paper envisions a vision where reliable LLM4Fuzz transforms the landscape of software testing and security for industry, software development practitioners, and economic accessibility. It then outlines a road ahead for future research, identifying key challenges and offering specific suggestions for the researchers to consider. This work strives to spark innovation in the field, positioning reliable LLM4Fuzz as a fundamental component of modern software testing.