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A Comprehensive Study of Exploitable Patterns in Smart Contracts: From Vulnerability to Defense

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arxiv 2504.21480 v1 pith:NARK3VAV submitted 2025-04-30 cs.CR cs.AIcs.SE

classification cs.CRcs.AIcs.SE
keywords contractssmartsecurityblockchaincomprehensiveethereumrisksvulnerability
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rapid advancement of blockchain technology, smart contracts have enabled the implementation of increasingly complex functionalities. However, ensuring the security of smart contracts remains a persistent challenge across the stages of development, compilation, and execution. Vulnerabilities within smart contracts not only undermine the security of individual applications but also pose significant risks to the broader blockchain ecosystem, as demonstrated by the growing frequency of attacks since 2016, resulting in substantial financial losses. This paper provides a comprehensive analysis of key security risks in Ethereum smart contracts, specifically those written in Solidity and executed on the Ethereum Virtual Machine (EVM). We focus on two prevalent and critical vulnerability types (reentrancy and integer overflow) by examining their underlying mechanisms, replicating attack scenarios, and assessing effective countermeasures.

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Cited by 3 Pith papers

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

  1. From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification

    cs.CR 2026-08 reject novelty 4.0 of 10

    An MLP with SMOTE and Focal Loss is claimed to detect rug pull tokens with 0.927 accuracy, but the ground truth labels are partly randomly generated.

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

    cs.CR 2026-07 conditional novelty 4.0 of 10

    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.

  3. Securing High-Concurrency Ticket Sales: A Framework Based on Microservice

    cs.SE 2025-12 reject novelty 2.0 of 10

    A railway ticketing system built from standard Spring Cloud components is reported to reach 817 req/s on a train-query interface, but only under a 100-thread local VM test with inconsistent purchase-interface data.

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