AuditGPT: Auditing Smart Contracts with ChatGPT
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Abstract (arXiv)
To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each containing a set of rules to guide the behaviors of smart contracts. Violating the ERC rules could cause serious security issues and financial loss, signifying the importance of verifying smart contracts follow ERCs. Today's practices of such verification are to either manually audit each single contract or use expert-developed, limited-scope program-analysis tools, both of which are far from being effective in identifying ERC rule violations. This paper presents a tool named AuditGPT that leverages large language models (LLMs) to automatically and comprehensively verify ERC rules against smart contracts. To build AuditGPT, we first conduct an empirical study on 222 ERC rules specified in four popular ERCs to understand their content, their security impacts, their specification in natural language, and their implementation in Solidity. Guided by the study, we construct AuditGPT by separating the large, complex auditing process into small, manageable tasks and design prompts specialized for each ERC rule type to enhance LLMs' auditing performance. In the evaluation, AuditGPT successfully pinpoints 418 ERC rule violations and only reports 18 false positives, showcasing its effectiveness and accuracy. Moreover, AuditGPT beats an auditing service provided by security experts in effectiveness, accuracy, and cost, demonstrating its advancement over state-of-the-art smart-contract auditing practices.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 3 of its 41 references that are in the map. First posted on arXiv 5 Apr 2024.
Builds on
3 of 41 references.
References · 3
- 2017One-Shot Imitation Learningcited by 232
- 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
- 2017Under-Optimized Smart Contracts Devour Your Moneycited by 9
Built on by
Citing papers per year: 2024: 3; 2025: 3; 2026: 3.
9 papers.
- 2025A Survey on Agentic Security: Applications, Threats and Defensescited by 17
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilitiescited by 2
- 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integrationcited by 2
- 2024Blockchain Data Analysis in the Era of Large-Language Modelscited by 2
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditingcited by 2
- 2026An Effective and Cost-Efficient Agentic Framework for Ethereum Smart Contract Auditingcited by 1
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarizationcited by 1
- 2026Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregationcited by 0
- 2026Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation2 Jun 2026
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization27 Mar 2026
- 2026An Effective and Cost-Efficient Agentic Framework for Ethereum Smart Contract Auditing25 Jan 2026
- 2025A Survey on Agentic Security: Applications, Threats and Defenses7 Oct 2025
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditing21 May 2025
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMs4 Feb 2025
- 2024Blockchain Data Analysis in the Era of Large-Language Models9 Dec 2024
- 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integration25 Nov 2024
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities15 Sep 2024