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Secure-by-design smart contract based on dataflow implementations

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arxiv 2309.17200 v2 pith:6LNIJIQU submitted 2023-09-29 cs.SI cs.PL

Secure-by-design smart contract based on dataflow implementations

classification cs.SI cs.PL
keywords contractsmartblockchainprogrammingarticledevelopmentsecurityvulnerabilities
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This article conducts an extensive examination of the persisting challenges related to smart contract attacks within blockchain networks, with a particular focus on the reentrancy attack. It emphasizes the inherent vulnerabilities embedded in the programming languages commonly employed for smart contract development, particularly within Ethereum Virtual Machine (EVM)-based blockchains. While the concrete example used primarily employs the Solidity programming language, the insights garnered from this study are readily generalizable to a wide array of blockchain architectures. Significantly, this article extends beyond the mere identification of vulnerabilities and ventures into the realm of proactive security measures. It explores the adaptation and adoption of dataflow programming paradigms, employing Domain-Specific Languages (DSLs) to enforce security by design in the context of smart contract development. This forward-looking approach aims to bolster the foundational principles of blockchain security, offering a promising research direction for mitigating the risks associated with smart contract vulnerabilities. The objective of this article is to cater to a diverse audience, ranging from individuals with limited computer science and programming expertise to seasoned experts in the field. It provides a comprehensive and accessible resource for fostering a deeper understanding of the intricate dynamics between blockchain technology and the imperative need for secure smart contract development practices.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Ethereum NFT Smart Contracts: Knowledge-Guided Vulnerability Detection with LLM and Code Slicing

    cs.CR 2026-07 conditional novelty 4.0

    Code slicing plus a knowledge base raises the LLM's positive-label rate from 73.8% to 97.1% on 450 NFT contracts, an effect not validated against ground truth.