EVuLLM: Ethereum Smart Contract Vulnerability Detection Using Large Language Models

Smart contracts have become integral to decentralized applications, yet their programmability introduces critical security risks, exemplified by high-profile exploits such as the DAO and Parity Wallet incidents. Existing vulnerability detection methods, including static and dynamic analysis, as well as machine learning-based approaches, often struggle with emerging threats and rely heavily on large, labeled datasets. This study investigates the effectiveness of open-source, lightweight large language models (LLMs) fine-tuned using parameter-efficient techniques, including Quantized Low-Rank Adaptation (QLoRA), for smart contract vulnerability detection. We introduce the EVuLLM dataset to address the scarcity of diverse evaluation resources and demonstrate that our fine-tuned models achieve up to 94.78% accuracy, surpassing the performance of larger proprietary models, while significantly reducing computational requirements. Moreover, we emphasize the advantages of lightweight models deployable on local hardware, such as enhanced data privacy, reduced reliance on internet connectivity, lower infrastructure costs, and improved control over model behavior, factors that are especially critical in security-sensitive blockchain applications. We also explore Retrieval-Augmented Generation (RAG) as a complementary strategy, achieving competitive results with minimal training. Our findings highlight the practicality of using locally hosted LLMs for secure, efficient, and reproducible smart contract analysis, paving the way for broader adoption of AI-driven security in blockchain ecosystems.