SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework

• URL: http://arxiv.org/abs/2411.19234v1
• Date: Thu, 28 Nov 2024 16:02:01 GMT
• Title: SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework
• Authors: Oualid Zaazaa, Hanan El Bakkali,
• Abstract summary: This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs) to advance smart contract vulnerability detection. We created a specialized dataset of five randomly selected vulnerabilities for model training and evaluation. Our results show an exact match accuracy of 91.1% with sufficient data, although GPT-4 demonstrated reduced performance compared to GPT-3 in rule generation.
• Score: 0.0
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• Abstract: Smart contracts are essential for managing digital assets in blockchain networks, highlighting the need for effective security measures. This paper introduces SmartLLMSentry, a novel framework that leverages large language models (LLMs), specifically ChatGPT with in-context training, to advance smart contract vulnerability detection. Traditional rule-based frameworks have limitations in integrating new detection rules efficiently. In contrast, SmartLLMSentry utilizes LLMs to streamline this process. We created a specialized dataset of five randomly selected vulnerabilities for model training and evaluation. Our results show an exact match accuracy of 91.1% with sufficient data, although GPT-4 demonstrated reduced performance compared to GPT-3 in rule generation. This study illustrates that SmartLLMSentry significantly enhances the speed and accuracy of vulnerability detection through LLMdriven rule integration, offering a new approach to improving Blockchain security and addressing previously underexplored vulnerabilities in smart contracts.

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