基于规则的误报减少方法增强智能合约分析符号执行工具的可靠性
Enhancing Reliability of Symbolic Execution Tools for Smart Contract Analysis through Rule-Based False Positive Reduction
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中文总结 AI 辅助
本文针对智能合约分析工具Mythril的误报问题，设计基于规则的误报减少方法，在不降低真实漏洞检测能力的前提下显著减少误报，提升了工具可靠性。
中文摘要 AI 辅助
区块链是一种去中心化、安全的账本系统，支持透明且不可篡改的记录保存，是数字交易中信任与安全的核心。智能合约是编码在区块链上的自执行协议，可自动让各方履行协议条款，在满足条件时触发对应操作，确保交易去中心化与透明化。由于缺乏标准化，编写可靠的智能合约颇具挑战。为查找安全漏洞，人们使用多种方法的工具，包括基于符号执行的工具。但这类工具常报告大量误报，引发对其可靠性的担忧，调查误报耗费的时间与精力会分散处理真实漏洞的资源，因此这类工具必须按其误报率进行评估，更重要的是，需增强工具所用算法与启发式方法，以区分真实漏洞与误报。本文首先展示了基于符号执行的以太坊智能合约分析工具Mythril生成的漏洞报告中误报的普遍性，分析了这些不准确的根本原因，并基于所得见解设计了一种基于规则的方法以减少误报。我们针对Mythril中影响最大的漏洞实现了规则，并评估了方法的有效性。结果显示，在不损害真实漏洞检测的情况下，误报大幅减少，从而增强了工具的可靠性。
英文摘要
A blockchain is a decentralized, secure ledger system that enables transparent and immutable record-keeping, essential for trust and security in digital transactions. Smart contracts are self-executing agreements encoded on a blockchain, enabling different parties to fulfill the terms of the agreement automatically. These contracts trigger corresponding actions when conditions are met, ensuring decentralized and transparent transactions. Writing reliable smart contracts is challenging due to the lack of standardization. To find security vulnerabilities, tools based on various approaches, including symbolic execution, are used. However, these tools often report a large number of false positives, raising concerns about their reliability. The time and effort spent investigating false positives diverts resources from addressing actual vulnerabilities. Therefore, such tools must also be evaluated according to the rate of false positives they exhibit. More importantly, the algorithms and heuristics used by the tools must be enhanced to distinguish between true vulnerabilities and false alarms. In this paper, we first demonstrate the prevalence of false positives in vulnerability reports generated by Mythril, a symbolic execution-based analysis tool for Ethereum smart contracts. We analyze the root causes of these inaccuracies and devise a rule-based approach based on the gained insight to reduce false positives. We implement our rules for the most impactful vulnerabilities in Mythril and assess the effectiveness of our approach. Our results show a significant reduction in false positives without compromising the detection of true vulnerabilities, thus enhancing the tool's reliability.
