research-article

Gigahorse: thorough, declarative decompilation of smart contracts

Authors:

Bernhard Scholz,

Yannis SmaragdakisAuthors Info & Claims

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

Pages 1176 - 1186

https://doi.org/10.1109/ICSE.2019.00120

Published: 25 May 2019 Publication History

Abstract

The rise of smart contracts---autonomous applications running on blockchains---has led to a growing number of threats, necessitating sophisticated program analysis. However, smart contracts, which transact valuable tokens and cryptocurrencies, are compiled to very low-level bytecode. This bytecode is the ultimate semantics and means of enforcement of the contract.

We present the Gigahorse toolchain. At its core is a reverse compiler (i.e., a decompiler) that decompiles smart contracts from Ethereum Virtual Machine (EVM) bytecode into a high-level 3-address code representation. The new intermediate representation of smart contracts makes implicit data- and control-flow dependencies of the EVM bytecode explicit. Decompilation obviates the need for a contract's source and allows the analysis of both new and deployed contracts.

Gigahorse advances the state of the art on several fronts. It gives the highest analysis precision and completeness among decompilers for Ethereum smart contracts---e.g., Gigahorse can decompile over 99.98% of deployed contracts, compared to 88% for the recently-published Vandal decompiler and under 50% for the state-of-the-practice Porosity decompiler. Importantly, Gigahorse offers a full-featured toolchain for further analyses (and a "batteries included" approach, with multiple clients already implemented), together with the highest performance and scalability. Key to these improvements is Gigahorse's use of a declarative, logic-based specification, which allows high-level insights to inform low-level decompilation.

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Li WLi XLi ZZhang YFilkov VRay BZhou M(2024)COBRA: Interaction-Aware Bytecode-Level Vulnerability Detector for Smart ContractsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695601(1358-1369)Online publication date: 27-Oct-2024
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Information & Contributors

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Published In

cover image ACM Conferences

ICSE '19: Proceedings of the 41st International Conference on Software Engineering

May 2019

1318 pages

General Chair:
Joanne M. Atlee
University of Waterloo, Canada
,
Program Chairs:
Tevfik Bultan
University of California, Santa Barbara
,
Jon Whittle
Monash University, Australia

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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IEEE Press

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Published: 25 May 2019

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ICSE '19: 41st International Conference on Software Engineering

May 25 - 31, 2019

Quebec, Montreal, Canada

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Li WLi XLi ZZhang YFilkov VRay BZhou M(2024)COBRA: Interaction-Aware Bytecode-Level Vulnerability Detector for Smart ContractsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695601(1358-1369)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695601
Grossman SToman JBakst AArora SSagiv MNandi C(2024)Practical Verification of Smart Contracts using Memory SplittingProceedings of the ACM on Programming Languages10.1145/36897968:OOPSLA2(2402-2433)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689796
Bembenek AGreenberg MChong S(2024)Making Formulog Fast: An Argument for Unconventional Datalog EvaluationProceedings of the ACM on Programming Languages10.1145/36897548:OOPSLA2(1219-1248)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689754
Li YSong WHuang J(2024)VarLifter: Recovering Variables and Types from Bytecode of Solidity Smart ContractsProceedings of the ACM on Programming Languages10.1145/36897118:OOPSLA2(1-29)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689711
Qin JWang YLu S(2024)Bytecode Ponzi Contract Detection Based on Cross-modal Mutual LearningProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671317(948-956)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671317
Ruggiero CMazzini PCoppa ELenti SBonomi S(2024)SoK: A Unified Data Model for Smart Contract Vulnerability TaxonomiesProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3664507(1-13)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3664507
Albert ECorreas JGordillo PRomán-Díez GRubio AChristakis MPradel M(2024)Synthesis of Sound and Precise Storage Cost Bounds via Unsound Resource Analysis and Max-SMTProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680352(1186-1197)Online publication date: 11-Sep-2024
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Ye MLin XNan YWu JZheng ZChristakis MPradel M(2024)Midas: Mining Profitable Exploits in On-Chain Smart Contracts via Feedback-Driven Fuzzing and Differential AnalysisProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680321(794-805)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680321
Zhang CWang LRigger M(2024)Finding Cross-Rule Optimization Bugs in Datalog EnginesProceedings of the ACM on Programming Languages10.1145/36498158:OOPSLA1(110-136)Online publication date: 29-Apr-2024
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Zhou YSun JMa FChen YYan ZJiang YRoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)Stop Pulling my Rug: Exposing Rug Pull Risks in Crypto Token to InvestorsProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639722(228-239)Online publication date: 14-Apr-2024
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