research-article

Making Smart Contracts Smarter

Authors:

Hrishi Olickel,

Prateek Saxena,

Aquinas HoborAuthors Info & Claims

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Pages 254 - 269

https://doi.org/10.1145/2976749.2978309

Published: 24 October 2016 Publication History

Abstract

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins.

In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

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https://doi.org/10.9728/dcs.2024.25.4.1087
Sawant RDeepa JMenon RWadalkar S(2024)Research Trends and Impacts of Blockchain Technology in Construction Sector: Scientistometric StudyData and Metadata10.56294/dm2024.3693Online publication date: 1-Jan-2024
https://doi.org/10.56294/dm2024.369
Sakib S(2024)Blockchain Technology for Smart ContractsAchieving Secure and Transparent Supply Chains With Blockchain Technology10.4018/979-8-3693-0482-2.ch014(246-266)Online publication date: 18-Jan-2024
https://doi.org/10.4018/979-8-3693-0482-2.ch014
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Index Terms

Making Smart Contracts Smarter
1. Security and privacy
  1. Formal methods and theory of security
    1. Logic and verification
  2. Software and application security
    1. Domain-specific security and privacy architectures
    2. Software security engineering

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Published In

cover image ACM Conferences

CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

October 2016

1924 pages

ISBN:9781450341394

DOI:10.1145/2976749

General Chairs:
Edgar Weippl
SBA Research, Austria
,
Stefan Katzenbeisser
TU Darmstadt, CYSEC, Germany
,
Program Chairs:
Christopher Kruegel
University of California, Santa Barbara, USA
,
Andrew Myers
Cornell University, USA
,
Shai Halevi
IBM Research, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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New York, NY, United States

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Published: 24 October 2016

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CCS'16

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CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security

October 24 - 28, 2016

Vienna, Austria

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CCS '16 Paper Acceptance Rate 137 of 831 submissions, 16%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '24

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ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

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Cited By

Cho D(2024)Smart Contract Security Vulnerability Analysis and Security Automation ModelJournal of Digital Contents Society10.9728/dcs.2024.25.4.108725:4(1087-1094)Online publication date: 30-Apr-2024
https://doi.org/10.9728/dcs.2024.25.4.1087
Sawant RDeepa JMenon RWadalkar S(2024)Research Trends and Impacts of Blockchain Technology in Construction Sector: Scientistometric StudyData and Metadata10.56294/dm2024.3693Online publication date: 1-Jan-2024
https://doi.org/10.56294/dm2024.369
Sakib S(2024)Blockchain Technology for Smart ContractsAchieving Secure and Transparent Supply Chains With Blockchain Technology10.4018/979-8-3693-0482-2.ch014(246-266)Online publication date: 18-Jan-2024
https://doi.org/10.4018/979-8-3693-0482-2.ch014
Sanchez-Gomez NGutierrez JParrilla EGarcía Jde-Acuña MEscalona M(2024)Mechanism for the Systematic Generation of Functional Tests of Smart Contracts in Digital Publication Management SystemsFrameworks for Blockchain Standards, Tools, Testbeds, and Platforms10.4018/979-8-3693-0405-1.ch008(182-207)Online publication date: 17-Jan-2024
https://doi.org/10.4018/979-8-3693-0405-1.ch008
De Mofo GWacka ATchakounte FFotso J(2024)Detection of Vulnerabilities in Cryptocurrency Smart Contracts Based on Image ProcessingGlobal Perspectives on the Applications of Computer Vision in Cybersecurity10.4018/978-1-6684-8127-1.ch004(102-123)Online publication date: 23-Feb-2024
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Begum KMozumder MJoo MKim H(2024)BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT NetworksSensors10.3390/s2414459124:14(4591)Online publication date: 15-Jul-2024
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Jin JZhan WLi HDing YLi J(2024)A Dynamic Behavior Verification Method for Composite Smart Contracts Based on Model CheckingMathematics10.3390/math1215243112:15(2431)Online publication date: 5-Aug-2024
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Yan LWang LLi GShao JXia Z(2024)Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT NetworksJournal of Marine Science and Engineering10.3390/jmse1209165612:9(1656)Online publication date: 16-Sep-2024
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Zhang JLu GYu J(2024)A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training TechniquesElectronics10.3390/electronics1318378613:18(3786)Online publication date: 23-Sep-2024
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Kiani RSheng V(2024)Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature ReviewElectronics10.3390/electronics1312229513:12(2295)Online publication date: 12-Jun-2024
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