Robust Detection and Analysis of Smart Contract Vulnerabilities with Large Language Model Agents
- 1 College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
- 2 School of Cybersecurity and Privacy, Georgia Institute of Technology, Atlanta, GA, USA
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.
- IBM (2024) What Are Smart Contracts on Blockchain?
- SWC Registry (2024) Smart Contract Weakness Classification (SWC) Registry, 2020.
- Wikipedia Contributors (2024) The DAO—Wikipedia.
- Luu, L., Chu, D., Olickel, H., Saxena, P. and Hobor, A. (2016) Making Smart Contracts Smarter. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , Vienna, 24-28 October 2016, 254-269. https://doi.org/10.1145/2976749.2978309
- Consensys Diligence (2019) What Is Mythril? Mythril v 0.23.9 Documentation.
- Mossberg, M., Manzano, F., Hennenfent, E., Groce, A., Grieco, G., Feist, J., et al . (2019) Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts. 2019 34 th IEEE / ACM International Conference on Automated Software Engineering ( ASE ), San Diego, 11-15 November 2019, 1186-1189. https://doi.org/10.1109/ase.2019.00133
- Feist, J., Grieco, G. and Groce, A. (2019) Slither: A Static Analysis Framework for Smart Contracts. 2019 IEEE / ACM 2 nd International Workshop on Emerging Trends in Software Engineering for Blockchain ( WETSEB ), Montreal, 27 May 2019, 8-15. https://doi.org/10.1109/wetseb.2019.00008
- Boi, B., Esposito, C. and Lee, S. (2024) Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM). ACM SIGAPP Applied Computing Review , 24, 19-29. https://doi.org/10.1145/3687251.3687253
- He, Z., Zhao, Z., Chen, K. and Liu, Y. (2024) Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms. Computers , Materials & Continua , 79, 3023-3045. https://doi.org/10.32604/cmc.2024.050281
- 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
- BasuMallick, C. (2023) Smart Contracts: Types, Benefits, and Tools. Spiceworks.
- OWASP Foundation (2024) OWASP Smart Contract Top 10.
- Trust Wallet (2024) What Is a Mempool in Crypto?
- Rossini, M. (2022) Slither Audited Smart Contracts Dataset. Hugging Face.
- Datadog (2024) What Is Static Analysis?
- Ethereum Foundation (2024) Web3.py: A Python Interface for Interacting with the Ethereum Blockchain and Ecosystem.