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arXiv 2406.00523cs.CR
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窃取信任:揭示Web3认证中的盲消息攻击

Stealing Trust: Unraveling Blind Message Attacks in Web3 Authentication

Kailun Yan, Xiaokuan Zhang, Wenrui Diao

AI总结:

本文揭示Web3认证中的盲消息攻击漏洞,开发了检测工具Web3AuthChecker和防护工具Web3AuthGuard,发现75.8%的真实部署存在风险,并获两项CVE编号。

摘要:

随着Web3领域的持续快速扩张,Web3认证的安全性——通常是通往各种Web3应用的门户——变得日益关键。尽管Web3认证被众多Web3应用广泛用作登录方式,但其安全风险尚未受到足够关注。本文研究了Web3认证过程中的漏洞,并提出了一种新型攻击,称为盲消息攻击。在盲消息攻击中,攻击者利用用户无法验证消息来源的弱点,诱骗用户盲目签署来自目标应用的消息,从而实现对目标应用的未授权访问。我们开发了Web3AuthChecker,这是一种动态检测工具,通过与Web3认证相关的API交互来识别漏洞。我们对真实世界Web3应用的评估显示,高达75.8%(22/29)的Web3认证部署面临盲消息攻击的风险。针对这一令人担忧的情况,我们在开源钱包MetaMask上实现了Web3AuthGuard,以提醒用户潜在的攻击。我们的评估结果表明,Web3AuthGuard能够在80%的测试Web3认证中成功发出警报。我们已负责任地向存在漏洞的网站报告了发现,并获得了两个CVE编号。

英文摘要:

As the field of Web3 continues its rapid expansion, the security of Web3 authentication, often the gateway to various Web3 applications, becomes increasingly crucial. Despite its widespread use as a login method by numerous Web3 applications, the security risks of Web3 authentication have not received much attention. This paper investigates the vulnerabilities in the Web3 authentication process and proposes a new type of attack, dubbed blind message attacks. In blind message attacks, attackers trick users into blindly signing messages from target applications by exploiting users' inability to verify the source of messages, thereby achieving unauthorized access to the target application. We have developed Web3AuthChecker, a dynamic detection tool that interacts with Web3 authentication-related APIs to identify vulnerabilities. Our evaluation of real-world Web3 applications shows that a staggering 75.8% (22/29) of Web3 authentication deployments are at risk of blind message attacks. In response to this alarming situation, we implemented Web3AuthGuard on the open-source wallet MetaMask to alert users of potential attacks. Our evaluation results show that Web3AuthGuard can successfully raise alerts in 80% of the tested Web3 authentications. We have responsibly reported our findings to vulnerable websites and have been assigned two CVE IDs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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