Detection of malicious smart contracts by fine‐tuning GPT‐3

Abstract

This paper introduces a comprehensive framework for the detection and identification of malicious smart contracts, emphasizing their vulnerabilities. The framework leverages the capabilities of GPT‐3, which have been adapted and fine‐tuned for binary and multi‐class classification tasks. To the best of our knowledge, this study is the first to explore the use of GPT‐3 specifically for detecting and identifying malicious smart contracts. The framework addresses previously unexplored research questions and provides insightful answers through rigorous experimentation. The contributions of this work include proposing a novel approach, pioneering the adaptation of GPT‐3 for this purpose, and offering valuable insights into the detection of malicious smart contracts and vulnerabilities. Notably, our research reveals that GPT‐3 excels not only in understanding natural language but also in decoding the secrets embedded in numerical codes like opcodes. This finding extends the applicability of GPT‐3 beyond language‐based tasks and highlights its potential in enhancing smart contract security.