{"title":"EOSIOAnalyzer:一个有效的EOSIO智能合约静态分析漏洞检测框架","authors":"Wenyuan Li, Jiahao He, Gansen Zhao, Jinji Yang, Shuangyin Li, Ruilin Lai, P. Li, Hua Tang, Haoyu Luo, Ziheng Zhou","doi":"10.1109/COMPSAC54236.2022.00124","DOIUrl":null,"url":null,"abstract":"EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"EOSIOAnalyzer: An Effective Static Analysis Vulnerability Detection Framework for EOSIO Smart Contracts\",\"authors\":\"Wenyuan Li, Jiahao He, Gansen Zhao, Jinji Yang, Shuangyin Li, Ruilin Lai, P. Li, Hua Tang, Haoyu Luo, Ziheng Zhou\",\"doi\":\"10.1109/COMPSAC54236.2022.00124\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.\",\"PeriodicalId\":330838,\"journal\":{\"name\":\"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC54236.2022.00124\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC54236.2022.00124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
EOSIO智能合约是可以由相互不信任的节点组成的网络共同执行的程序。由于EOSIO智能合约管理着有价值的资产,它们成为高价值目标,并受到越来越多的攻击。保护EOSIO智能合约的工具是必不可少的。本文提出EOSIOAnalyzer,这是一个有效的静态安全分析框架,用于EOSIO智能合约,以对抗三种最常见的攻击。该框架由控制流图构建器、静态分析器和漏洞检测器三个部分组成。本文实现了一种将低级Wasm字节码转换为高级中间表示(寄存器传输语言)的方法。此外,本文还对Fake EOS Transfer、Forged Transfer Notification和Block Information Dependency三种流行的EOSIO智能合约漏洞实现了漏洞检测规范。作为概念验证,本文通过实验来评估EOSIOAnalyzer的有效性和效率。实验结果表明,三个漏洞的检测准确率分别为100%、98.8%和100%。
EOSIOAnalyzer: An Effective Static Analysis Vulnerability Detection Framework for EOSIO Smart Contracts
EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.