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

2025-03-02

AI Summary

LLM4Fuzz faces critical reliability bottlenecks—including low driver effectiveness, poor seed quality, and insufficient seed diversity—that hinder industrial deployment. This paper systematically identifies its core reliability challenges for the first time and proposes a three-layer trustworthiness framework covering driver generation, seed optimization, and evaluation feedback. Integrating prompt engineering, program analysis, fuzzing feedback, and multi-dimensional seed assessment, we design an evolution path that is verifiable, reproducible, and integrable. We distill five key technical challenges and their corresponding research directions. Our goal is to achieve >80% driver compilation success rate and a +30% improvement in code coverage. The work establishes both theoretical foundations and practical guidelines for building highly reliable LLM4Fuzz systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Address reliability challenges in LLM-driven fuzz testing.
• Improve driver validity and seed quality in LLM4Fuzz.
• Explore research directions for practical LLM4Fuzz adoption.

Innovation

• LLM-driven fuzz testing automation
• Improving driver validity and seed quality
• Envisioning reliable LLM4Fuzz for software security