mlsanityData to 14 Oct 2026

SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework

arXiv 2411.19234 ↗First posted 28 Nov 2024cs.CR cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 61 references that are in the map. First posted on arXiv 28 Nov 2024.

Builds on

35 of 61 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026SPEAR: An Engineering Case Study of Multi-Agent Coordination for Smart Contract Auditingcited by 0
  2. 2026Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contractscited by 0
  3. 2026Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregationcited by 0