[论文解读] The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts

Christof Ferreira Torres, Mathis Steichen+1 more|arXiv (Cornell University)|Feb 19, 2019

Blockchain Technology Applications and Security参考文献 31被引用 51

一句话总结

本论文系统分析以太坊中的诱饵骗局，开发 HoneyBadger 通过符号执行和启发式方法来检测它们，并报告关于盛行率、技术和盈利能力的大规模发现。

ABSTRACT

Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across a decentralised network of nodes. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise, where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying seemingly vulnerable contracts that contain hidden traps. This new type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypot smart contracts, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HoneyBadger - a tool that employs symbolic execution and well defined heuristics to expose honeypots. We perform a large-scale analysis on more than 2 million smart contracts and show that our tool not only achieves high precision, but is also highly efficient. We identify 690 honeypot smart contracts as well as 240 victims in the wild, with an accumulated profit of more than $90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots.

研究动机与目标

• 在以太坊智能合约中定义诱饵并阐明其对用户和生态系统的风险。
• 建立诱饵技术分类法，突出基于 SMT 的检测挑战。
• 创建 HoneyBadger，这是一个结合符号执行和启发式方法以检测诱饵合约的工具。
• 对以太坊字节码进行大规模分析，以量化诱饵的盛行率和影响。

提出的方法

• 构建按运行层级分类的诱饵技术分类法：以太坊虚拟机、Solidity 编译器和 Etherscan 浏览器。
• 开发 HoneyBadger，这是一个基于 Python 的工具，使用符号执行（通过字节码的 CFG）和 Z3 SMT 求解器来分析路径并检测资金流动。
• 进行现金流分析，以判断合约是否能够接收和转移资金。
• 将诱饵检测器实现为与每个分类技术相关联的启发式方法（例如 Balance Disorder、Inheritance Disorder、Skip Empty String Literal 等等）。
• 在以太坊合约数据集上评估 HoneyBadger，以评估其精准性和可扩展性。

实验结果

研究问题

• RQ1 以太坊智能合约中常见的诱饵技术有哪些？
• RQ2 以太坊区块链上诱饵合约的盛行程度如何？
• RQ3 是否可以使用工具在高精度和高效率下自动检测诱饵模式？
• RQ4 部署的诱饵可能的盈利性和影响是什么？

主要发现

• 在野外识别到 690 个独立的诱饵合约与 240 名受害者。
• 诱饵的累计利润超过 $90,000。
• 人工验证显示报道的合约中有 87% 属于诱饵。
• 分析超过 200 万个智能合约以评估盛行率和特征。
• HoneyBadger 取得了高精度并实现了高效的大规模分析。
• 提出了分类法和工具化方法，能够实现自动化诱饵检测。