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Ma, W., Wu, D., Sun, Y., Wang, T., Liu, S., Zhang, J., Xue, Y. and Liu, Y. (2024) Combining Finetuning and LLM-Based Agents for Intuitive Smart Contract Auditing with Justifications.
https://arxiv.org/abs/2403.16073

has been cited by the following article:

• TITLE: Robust Detection and Analysis of Smart Contract Vulnerabilities with Large Language Model Agents

  AUTHORS: Nishank P. Kuppa, Vijay K. Madisetti

  KEYWORDS: Blockchain, Ethereum, Smart Contracts, Security, Decentralized Applications, Web3, Cryptocurrency, Large Language Models

  JOURNAL NAME: Journal of Information Security, Vol.16 No.1, January 26, 2025

  ABSTRACT: Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.