Leveraging Large Language Models and Machine Learning for Smart Contract Vulnerability Detection

TL;DR

This paper demonstrates that fine-tuned large language models significantly outperform traditional machine learning models in detecting various vulnerabilities in smart contract code, achieving over 90% accuracy and enhancing blockchain security.

Contribution

It introduces a novel approach of fine-tuning large language models specifically for smart contract vulnerability detection, surpassing existing benchmarks and providing insights into model strengths.

Findings

Fine-tuned LLMs achieve over 90% accuracy in vulnerability detection.

LLMs outperform traditional ML models in identifying subtle code vulnerabilities.

The approach enhances the security and robustness of smart contracts in blockchain systems.

Abstract

As blockchain technology and smart contracts become widely adopted, securing them throughout every stage of the transaction process is essential. The concern of improved security for smart contracts is to find and detect vulnerabilities using classical Machine Learning (ML) models and fine-tuned Large Language Models (LLM). The robustness of such work rests on a labeled smart contract dataset that includes annotated vulnerabilities on which several LLMs alongside various traditional machine learning algorithms such as DistilBERT model is trained and tested. We train and test machine learning algorithms to classify smart contract codes according to vulnerability types in order to compare model performance. Having fine-tuned the LLMs specifically for smart contract code classification should help in getting better results when detecting several types of well-known vulnerabilities, such as Reentrancy, Integer Overflow, Timestamp Dependency and Dangerous Delegatecall. From our initial experimental results, it can be seen that our fine-tuned LLM surpasses the accuracy of any other model by achieving an accuracy of over 90%, and this advances the existing vulnerability detection benchmarks. Such performance provides a great deal of evidence for LLMs ability to describe the subtle patterns in the code that traditional ML models could miss. Thus, we compared each of the ML and LLM models to give a good overview of each models strengths, from which we can choose the most effective one for real-world applications in smart contract security. Our research combines machine learning and large language models to provide a rich and interpretable framework for detecting different smart contract vulnerabilities, which lays a foundation for a more secure blockchain ecosystem.