SmartLLM: Smart Contract Auditing using Custom Generative AI

Smart contracts, integral to decentralized finance (DeFi) and blockchain ecosystems, 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, resulting in 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 and zero-shot LLM prompting (e.g., ChatGPT-3.5 and GPT-4). Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70.0%, underscoring the model’s robustness in identifying vulnerabilities, including re-entrancy 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.