LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection

TL;DR

This paper presents LLM-SmartAudit, a novel framework using large language models and multi-agent collaboration to detect a wide range of smart contract vulnerabilities with higher accuracy and efficiency than traditional tools.

Contribution

Introduces LLM-SmartAudit, a comprehensive LLM-based system employing multi-agent collaboration for improved smart contract vulnerability detection.

Findings

Outperforms traditional auditing tools in accuracy and efficiency

Detects complex logic vulnerabilities overlooked by existing tools

Validated on diverse datasets showing practical effectiveness

Abstract

The immutable nature of blockchain technology, while revolutionary, introduces significant security challenges, particularly in smart contracts. These security issues can lead to substantial financial losses. Current tools and approaches often focus on specific types of vulnerabilities. However, a comprehensive tool capable of detecting a wide range of vulnerabilities with high accuracy is lacking. This paper introduces LLM-SmartAudit, a novel framework leveraging the advanced capabilities of Large Language Models (LLMs) to detect and analyze vulnerabilities in smart contracts. Using a multi-agent conversational approach, LLM-SmartAudit employs a collaborative system with specialized agents to enhance the audit process. To evaluate the effectiveness of LLM-SmartAudit, we compiled two distinct datasets: a labeled dataset for benchmarking against traditional tools and a real-world dataset for assessing practical applications. Experimental results indicate that our solution outperforms all traditional smart contract auditing tools, offering higher accuracy and greater efficiency. Furthermore, our framework can detect complex logic vulnerabilities that traditional tools have previously overlooked. Our findings demonstrate that leveraging LLM agents provides a highly effective method for automated smart contract auditing.