Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM)

Abstract

Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This paper presents a novel approach to smart contract vulnerability detection utilizing Large Language Models (LLMs), as a tool to detect all the vulnerabilities at once. Our proposed tool leverages the advanced natural language processing capabilities of LLMs to analyze smart contract code and identify potential security flaws. By training the LLM on a diverse dataset of known smart contract vulnerabilities and secure coding practices, we enhance its ability to recognize subtle and complex vulnerabilities that traditional static analysis tools might miss. The evaluation of our tool demonstrates its effectiveness in detecting a wide range of vulnerabilities with satisfaction and accuracy, providing developers with a robust mechanism to improve the security of their smart contracts before deployment. This approach signifies a significant advancement in the application of artificial intelligence for blockchain security, highlighting the potential of LLMs to enhance the reliability and safety of decentralized applications.