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This paper addresses the high incidence of false positives in vulnerability reports generated by Mythril, a symbolic execution tool for Ethereum smart contracts, which undermines the tool's reliability. By analyzing the root causes of these inaccuracies, the authors develop a rule-based approach to effectively distinguish between true vulnerabilities and false alarms. The implementation of these rules results in a significant reduction of false positives while maintaining the detection rate of actual vulnerabilities, thereby enhancing the overall reliability of the tool.
Reducing false positives in smart contract analysis tools could save developers countless hours previously wasted on investigating non-issues.
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.
