WANA: Symbolic Execution of Wasm Bytecode for Extensible Smart Contract Vulnerability Detection

被引:12
|
作者
Jiang, Bo [1 ]
Chen, Yifei [1 ]
Wang, Dong [1 ]
Ashraf, Imran [2 ]
Chan, W. K. [2 ]
机构
[1] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China
[2] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
基金
国家重点研发计划;
关键词
WASM bytecode; Symbolic Execution; Smart Contract; Vulnerability Detection;
D O I
10.1109/QRS54544.2021.00102
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Many popular blockchain platforms support smart contracts for building decentralized applications. However, the vulnerabilities within smart contracts have demonstrated to lead to serious financial loss to their end users. In particular, the smart contracts on EOSIO smart contract platform have resulted in the loss of around 380K EOS tokens, which was around 1.9 million worth of USD at the time of attack. The EOSIO smart contract platform is based on the Wasm VM, which is also the underlying system supporting other smart contract platforms as well as Web application. In this work, we present WANA, an extensible smart contract vulnerability detection tool based on the symbolic execution for Wasm bytecode. WANA proposes a set of algorithms to detect the vulnerabilities in EOSIO smart contracts based on Wasm bytecode analysis. Our experimental analysis shows that WANA can effectively and efficiently detect vulnerabilities in EOSIO smart contracts. Furthermore, our case study also demonstrates that WANA can be extended to effectively detect vulnerabilities in Ethereum smart contracts.
引用
收藏
页码:926 / 937
页数:12
相关论文
共 50 条
  • [41] CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model
    Zhang, Lejun
    Chen, Weijie
    Wang, Weizheng
    Jin, Zilong
    Zhao, Chunhui
    Cai, Zhennao
    Chen, Huiling
    SENSORS, 2022, 22 (09)
  • [42] Smart contract: a survey towards extortionate vulnerability detection and security enhancement
    S. Porkodi
    D. Kesavaraja
    Wireless Networks, 2024, 30 : 1285 - 1304
  • [43] Smart Contract Vulnerability Detection Based on Code Graph Embedding Approach
    Zhai, Yiwen
    Yang, Jia
    Zhang, Mingwu
    FRONTIERS IN CYBER SECURITY, FCS 2024, PT I, 2024, 2315 : 317 - 332
  • [44] A Survey on Ethereum Smart Contract Vulnerability Detection Using Machine Learning
    Surucu, Onur
    Yeprem, Uygar
    Wilkinson, Connor
    Hilal, Waleed
    Gadsden, S. Andrew
    Yawney, John
    Alsadi, Naseem
    Giuliano, Alessandro
    DISRUPTIVE TECHNOLOGIES IN INFORMATION SCIENCES VI, 2022, 12117
  • [45] An integrated deep learning model for Ethereum smart contract vulnerability detection
    Vikas Kumar Jain
    Meenakshi Tripathi
    International Journal of Information Security, 2024, 23 : 557 - 575
  • [46] Bytecode Similarity Detection of Smart Contract across Optimization Options and Compiler Versions Based on Triplet Network
    Zhu, Di
    Yue, Feng
    Pang, Jianmin
    Zhou, Xin
    Han, Wenjie
    Liu, Fudong
    ELECTRONICS, 2022, 11 (04)
  • [47] Cache-Based Side-Channel Vulnerability Detection Based on Symbolic Execution
    Yang C.
    Guo Y.-F.
    Hu H.-C.
    Liu W.-Y.
    Huo S.-M.
    Wang Y.-W.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2019, 47 (06): : 1194 - 1200
  • [48] A model-guided symbolic execution approach for network protocol implementations and vulnerability detection
    Wen, Shameng
    Meng, Qingkun
    Feng, Chao
    Tang, Chaojing
    PLOS ONE, 2017, 12 (11):
  • [49] Machine-learning Approach using Solidity Bytecode for Smart-contract Honeypot Detection in the Ethereum
    Hara, Kazuki
    Takahashi, Takeshi
    Ishimaki, Motoya
    Omote, Kazumasa
    2021 21ST INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY COMPANION (QRS-C 2021), 2021, : 652 - 659
  • [50] A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules
    Liu, Zhenpeng
    Jiang, Mingxiao
    Zhang, Shengcong
    Zhang, Jialiang
    Liu, Yi
    IEEE ACCESS, 2023, 11 : 77990 - 77999