SmartLLM: Smart Contract Auditing using Custom Generative AI

TL;DR

SmartLLM leverages fine-tuned LLaMA 3.1 with Retrieval-Augmented Generation to significantly improve smart contract vulnerability detection accuracy and efficiency over traditional static analysis and generic LLM prompting methods.

Contribution

This paper introduces SmartLLM, a novel AI-based approach using domain-specific fine-tuning and retrieval techniques for more accurate smart contract auditing.

Findings

Achieves 100% recall in vulnerability detection

Outperforms Mythril, Slither, GPT-3.5, and GPT-4 in accuracy

Effectively detects reentrancy and access control issues

Abstract

Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, leading to high false-positive rates and an inability to detect dynamic vulnerabilities. This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) to enhance the accuracy and efficiency of smart contract auditing. By integrating domain-specific knowledge from ERC standards and employing advanced techniques such as QLoRA for efficient fine-tuning, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither, as well as zero-shot large language model (LLM) prompting methods such as GPT-3.5 and GPT-4. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities, including reentrancy and access control issues. This research advances smart contract security by offering a scalable and effective auditing solution, supporting the secure adoption of decentralized applications.