WALLETRADAR: Towards Automating the Detection of Vulnerabilities in Browser-based Cryptocurrency Wallets
Pengcheng Xia, Yanhui Guo, Zhaowen Lin, Jun Wu, Pengbo Duan, Ningyu, He, Kailong Wang, Tianming Liu, Yinliang Yue, Guoai Xu, Haoyu Wang

TL;DR
This paper introduces WALLETRADAR, an automated framework that uses static and dynamic analysis to detect security vulnerabilities in browser-based cryptocurrency wallets, significantly improving security assessment efficiency.
Contribution
The paper presents a novel automated detection tool for wallet vulnerabilities, including a comprehensive vulnerability taxonomy and an effective analysis framework.
Findings
Successfully detected vulnerabilities in 90% of tested wallets
Discovered 116 vulnerabilities across 70 wallets
Confirmed vulnerabilities led to bug bounties and fixes
Abstract
Cryptocurrency wallets, acting as fundamental infrastructure to the blockchain ecosystem, have seen significant user growth, particularly among browser-based wallets (i.e., browser extensions). However, this expansion accompanies security challenges, making these wallets prime targets for malicious activities. Despite a substantial user base, there is not only a significant gap in comprehensive security analysis but also a pressing need for specialized tools that can aid developers in reducing vulnerabilities during the development process. To fill the void, we present a comprehensive security analysis of browser-based wallets in this paper, along with the development of an automated tool designed for this purpose. We first compile a taxonomy of security vulnerabilities resident in cryptocurrency wallets by harvesting historical security reports. Based on this, we design WALLETRADAR, an…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Spam and Phishing Detection
