SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1145/3696410.3714790
Citations
1
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
3.25

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article

SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract

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1 citations
Blockchain Technology Applications and Security
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article

SuiGPT MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract

Eason Chen, Konstantinos Kryptos Chalkias, Xinyi Tang, Chuangji Li, Siyun Wang, Teresa Wu, Zimo Xiao, Shaoli Li
article en
1 citations

Abstract

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk.While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, such as Sui, hinders the ease of auditing.A promising approach to this issue is the use of a decompiler to reverse-engineer smart contract bytecode.However, existing decompilers for Sui produce code that is difficult to understand and cannot be directly recompiled.To address this, we developed the SuiGPT Move AI Decompiler (MAD), a Large Language Model (LLM)-powered web application that decompiles smart contract bytecodes on Sui into logically correct, human-readable, and recompilable source code with prompt engineering.Our evaluation shows that MAD's output successfully passes original unit tests and achieves a 73.33% recompilation success rate on real-world smart contracts.Additionally, newer models tend to deliver improved performance, suggesting that MAD's approach will become increasingly effective as LLMs continue to advance.In a user study involving 12 developers, we found that MAD significantly reduced the auditing workload compared to using traditional decompilers.Participants found MAD's outputs comparable to the original source code, improving accessibility for understanding and auditing non-open-source smart contracts.Through qualitative interviews with these developers and Web3 projects, we further discussed the strengths and concerns of MAD.MAD has practical implications for blockchain smart contract transparency, auditing, and education.It empowers users to easily and independently review and audit non-open-source smart contracts, fostering accountability and decentralization.Moreover, MAD's methodology could potentially extend to other smart contract languages, like Solidity, further enhancing Web3 transparency.

Carnegie Mellon University (US)
Alfred P. Sloan Foundation, Carnegie Mellon University
Decent work and economic growth
Openalex Percentile: Top 10%
Blockchain Technology Applications and Security
3.25
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