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AuditGPT: Auditing Smart Contracts with ChatGPT

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arxiv 2404.04306 v1 pith:ABJGTTM4 submitted 2024-04-05 cs.CR cs.AIcs.CLcs.CY

classification cs.CRcs.AIcs.CLcs.CY
keywords auditgptauditingcontractssmartrulesrulesecurityaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization

    cs.CR 2026-03 conditional novelty 6.0 of 10

    Knowdit links abstract DeFi semantics to vulnerability patterns in a knowledge graph and drives an agentic specify–harness–fuzz–reflect loop that finds all high-severity and most medium-severity bugs on held-out Code4...

  2. A Systematic Classification of Vulnerabilities in MoveEVM Smart Contracts (MWC)

    cs.CR 2025-05 reject novelty 5.0 of 10

    The paper introduces the MoveEVM Weakness Classification (MWC), a 37-code, six-frame taxonomy for Move-EVM hybrid smart contract vulnerabilities, but its empirical validation and internal consistency are lacking.

  3. Adaptive Plan-Execute Framework for Smart Contract Security Auditing

    cs.CR 2025-05 reject novelty 5.0 of 10

    SmartAuditFlow claims 100% detection on a standard smart contract benchmark and all 13 tested CVEs via a plan-execute LLM workflow, though the supporting evaluation has major reproducibility and validation gaps.

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