Unveiling the Landscape of Smart Contract Vulnerabilities: A Detailed Examination and Codification of Vulnerabilities in Prominent Blockchains
- URL: http://arxiv.org/abs/2312.00499v1
- Date: Fri, 1 Dec 2023 11:01:06 GMT
- Title: Unveiling the Landscape of Smart Contract Vulnerabilities: A Detailed Examination and Codification of Vulnerabilities in Prominent Blockchains
- Authors: Oualid Zaazaa, Hanan El Bakkali,
- Abstract summary: In this paper, we propose the most complete list of smart contract vulnerabilities with a detailed explanation of each one of them.
In addition, we propose a new codification system that facilitates the communication of those vulnerabilities between developers and researchers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the rise in using immature smart contract programming languages to build a decentralized application, more vulnerabilities have been introduced to the Blockchain and were the main reasons behind critical financial losses. Moreover, the immutability of Blockchain technology makes deployed smart contracts unfixable for the whole life of the Blockchain itself. The lack of complete and up-to-date resources that explain those vulnerabilities in detail has also contributed to increasing the number of vulnerabilities in Blockchain. In addition, the lack of a standardized nomination of the existing vulnerabilities has made redundant research and made developers more confused. Therefore, in this paper, we propose the most complete list of smart contract vulnerabilities that exist in the most popular Blockchains with a detailed explanation of each one of them. In addition, we propose a new codification system that facilitates the communication of those vulnerabilities between developers and researchers. This codification, help identify the most uncovered vulnerabilities to focus on in future research. Moreover, the discussed list of vulnerabilities covers multiple Blockchain and could be used for even future built Blockchains.
Related papers
- Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- Blockchain and Applications [17.293955748551053]
The article begins with an introduction to cryptography fundamentals.
It covers topics such as proof-of-work, proof-of-stake, and smart contracts.
The article concludes by addressing the current state of academic research on blockchain.
arXiv Detail & Related papers (2024-10-14T02:56:36Z) - BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
BlockFound is a customized foundation model for anomaly blockchain transaction detection.
We introduce a series of customized designs to model the unique data structure of blockchain transactions.
BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
arXiv Detail & Related papers (2024-10-05T05:11:34Z) - The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols.
Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol.
We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
arXiv Detail & Related papers (2024-07-16T20:53:04Z) - Cross-Blockchain Communication Using Oracles With an Off-Chain Aggregation Mechanism Based on zk-SNARKs [0.716879432974126]
We propose an oracle with an off-chain aggregation mechanism based on ZeroKnowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication.
The proposed solution only requires constant 378 kgas to submit data on the blockchain and is primarily independent of the underlying technology of the queried blockchains.
arXiv Detail & Related papers (2024-05-14T07:48:19Z) - A Novel Classification of Attacks on Blockchain Layers: Vulnerabilities, Attacks, Mitigations, and Research Directions [0.8540657305162735]
This survey proposes a novel classification of blockchain attacks and an in-depth investigation of blockchain data security.
We reveal the deep dynamics of these security concerns by closely investigating the fundamental causes of attacks at various blockchain tiers.
We also discuss the implications of quantum computing in blockchain and the weaknesses in the current technology that can be exploited in the future.
arXiv Detail & Related papers (2024-04-28T06:40:50Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - Architectural Design for Secure Smart Contract Development [0.0]
Several attacks on blockchain infrastructures have resulted in hundreds of millions of dollars lost and sensitive information compromised.
I identify common software vulnerabilities and attacks on blockchain infrastructures.
I propose a model for ensuring a stronger security standard for future systems leveraging smart contracts.
arXiv Detail & Related papers (2024-01-03T18:59:17Z) - Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of
Educational Blockchain [4.239144309557045]
This paper proposes a graph neural network based vulnerability detection for smart contracts in educational blockchains.
The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts.
arXiv Detail & Related papers (2023-03-08T09:58:58Z) - Quantum-resistance in blockchain networks [46.63333997460008]
This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Quantum Computing (CQC), and Tecnologico de Monterrey to identify and eliminate quantum threats in blockchain networks.
The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms.
arXiv Detail & Related papers (2021-06-11T23:39:25Z) - ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep
Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable.
We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts.
We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.