Adding six rule-based filters to Mythril cut false positives by 50.0% on clean contracts and 89.2% on vulnerable contracts in the Gigahorse benchmark, while keeping true positives roughly unchanged.
Empirical Vulnerability Analysis of Automated Smart Contracts Security Testing on Blockchains
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abstract
The emerging blockchain technology supports decentralized computing paradigm shift and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of software, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specific languages and its testability. There have been high-profile incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating financial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. This implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specific goal to more readily realize the application of blockchain smart contracts in security and privacy, we should first understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specific programming language, Solidity to provide the first...
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Enhancing Reliability of Symbolic Execution Tools for Smart Contract Analysis through Rule-Based False Positive Reduction
Adding six rule-based filters to Mythril cut false positives by 50.0% on clean contracts and 89.2% on vulnerable contracts in the Gigahorse benchmark, while keeping true positives roughly unchanged.