# LLM4Fuzz: Guided Fuzzing of Smart Contracts with Large Language Models

arXiv: https://arxiv.org/abs/2401.11108 · HTML: https://mlsanity.com/paper/2401.11108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [Detecting Buggy Contracts via Smart Testing](https://mlsanity.com/paper/2409.04597/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs](https://mlsanity.com/paper/2410.13918/) (2024)
- [SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts](https://mlsanity.com/paper/2610.18745/) (2026)

## Abstract (arXiv)

As blockchain platforms grow exponentially, millions of lines of smart contract code are being deployed to manage extensive digital assets. However, vulnerabilities in this mission-critical code have led to significant exploitations and asset losses. Thorough automated security analysis of smart contracts is thus imperative. This paper introduces LLM4Fuzz to optimize automated smart contract security analysis by leveraging large language models (LLMs) to intelligently guide and prioritize fuzzing campaigns. While traditional fuzzing suffers from low efficiency in exploring the vast state space, LLM4Fuzz employs LLMs to direct fuzzers towards high-value code regions and input sequences more likely to trigger vulnerabilities. Additionally, LLM4Fuzz can leverage LLMs to guide fuzzers based on user-defined invariants, reducing blind exploration overhead. Evaluations of LLM4Fuzz on real-world DeFi projects show substantial gains in efficiency, coverage, and vulnerability detection compared to baseline fuzzing. LLM4Fuzz also uncovered five critical vulnerabilities that can lead to a loss of more than 247k.