Smart Contract Vulnerabilities Research Articles

Security of the Secp256k1 Elliptic Curve used in the Bitcoin Blockchain

The article delves into the intricate characteristics and security properties of the secp256k1 elliptic curve used for the generation of addresses in the Bitcoin blockchain. The Bitcoin blockchain is a decentralized digital ledger that records all transactions made with Bitcoin cryptocurrency. In this work, the secp256k1 elliptic curve and its parameters and the method of generating private and public keys using random numbers are described. While the private key allows for the signing of transactions to spend Bitcoin, the corresponding public key and address enable others to verify transactions and send funds to that specific address on the blockchain, ensuring security, authenticity, and privacy in the decentralized network. The attacks on the use of secp256k1 for generating the bitcoin addresses like the Brute force attack, twist attack, fault attacks, and side channel attacks in the implementation of the elliptic curve are discussed. By maintaining the security and integrity of secp256k1, we can ensure that cryptographic operations, such as digital signatures and key exchanges, remain uncompromised. If the curve's security were compromised, malicious users could potentially derive private keys from public keys, leading to unauthorized transactions, double-spending, or other malicious activities. The security of implementation can be enhanced by ensuring cryptographic libraries and software implementations that utilize secp256k1 undergo thorough testing and validation to ensure correct and secure operations. The important attacks on blockchain technology like the 51% attack, Sybil attack, Double Spending attack, and Smart Contract vulnerabilities are discussed. Through a comprehensive exploration, readers will gain insights into why this particular elliptic curve was chosen for use in Bitcoin's cryptographic protocols, highlighting its role in ensuring the robustness and integrity of the blockchain ecosystem.

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.

Detection of vulnerabilities in blockchain smart contracts using deep learning

Detection of vulnerabilities in blockchain smart contracts using deep learning

Reentrancy vulnerability detection based on graph convolutional networks and expert patterns under subspace mapping

Smart contracts with automatic execution capability provide a vast development space for transactions in Blockchain. However, due to the vulnerabilities in smart contracts, Blockchain has suffered huge economic losses, which greatly undermines people’s trust in Blockchain and smart contracts. In this paper, we explore a vulnerability detection method based on graph neural networks and combine both contract source code and opcode. The structure of the method consists of four modules, i.e., preprocessing, subspace mapping, feature extraction, and detection modules. In the feature mapping module, we use a multi-subspace mapping approach to explore the impact of different subspace mappings on the detection method. For reentrancy vulnerability, we conducted extensive experiments. The experiments prove that our method achieves 95% accuracy and 94% F1-Score on average.

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Objective: Using a variety of datasets from the Ethereum documentation and Smart Contract Dataset repository, this study tackles the crucial problem of classifying smart contract vulnerabilities. Methods: Our study uses a three-module method and focuses on the Resource 3 Dataset, which contains over 2,000 Ethereum smart contracts, including inherited contracts. The groundwork for deep learning model training is laid in Module 1 by extracting bytecode from Solidity files and creating images thereafter. In Colab, Module 2 entails importing data, pre-processing, SMOTE balancing, and building three deep learning models: CNN, XCEPTION, and EfficientNet-B2. Module 3 is a Flask-based web application created in Visual Studio Code that enables vulnerability predictions, bytecode extraction, and user interaction. Findings: With an overall accuracy of 71 percent, the Convolutional Neural Network (CNN) displays its effectiveness in classifying vulnerabilities. Although the accuracy of XCEPTION and EfficientNet-B2 is 69% and 75%, respectively, the latter is the top performer. Novelty & Applications: The online application adds to the comprehensive examination of smart contract security by giving users an easy-to-use interface. The EfficientNet-B2 model stands out as a dependable tool for precise vulnerability classification, and this study advances our understanding of and efforts to mitigate vulnerabilities in Ethereum smart contracts. Keywords: Smart Contracts, Vulnerability Classification, Ethereum, Deep Learning, Convolutional Neural Network (CNN)

Examination of Approaches for Identifying Vulnerabilities in Smart Contracts

Objective: By reviewing various previous works, this paper collects the multiple of approaches, strategies used to identify vulnerabilities in smart contracts. Blockchain is a decentralized technology that securely and immutably, records transactions across numerous computers in a visible manner. On a blockchain, smart contracts are self-executing agreements that independently execute and verify contract conditions. This reduces the need for middlemen and increases transparency. Smart contract vulnerabilities are problems in the code that could allow other parties to gain access to, alter, or steal assets as a result of mistakes, faults or imperfections made during development, thereby causing financial and operational harm. In this paper we have algorithms, techniques to detect vulnerabilities in smart contract using deep learning found in literature surveys. Methods: We have found some techniques using opcode, bytecode, Skip-Gram-Word2Vec to convert the smart contract file. Findings: We have found that LSTM, Vanilla-RNN, GRU have very less accuracy 49.64,53.68,54.54. Novelty & Applications: We will come with some different algorithms that will understand different vulnerability with more accuracy. We have come with CNN, Xception, EfficientNet-B2 which has accuracy high then LSTM, Vanilla-RNN, GRU i.e.71,69,75 percent.

Blockchain and Cryptocurrency Security: Challenges, Innovations, and Future Directions

Blockchain technology and cryptocurrencies have emerged as transformative innovations with the potential to revolutionize various sectors, including finance, supply chain management, and healthcare. However, their widespread adoption also brings forth significant security challenges. This paper provides an overview of the security issues facing blockchain and cryptocurrency systems, explores current research efforts to address these challenges, and discusses potential future directions in blockchain and cryptocurrency security research. We examine topics such as secure consensus mechanisms, smart contract vulnerabilities, privacy-preserving techniques, and regulatory considerations. By understanding these challenges and exploring innovative solutions, we aim to foster the development of more robust and secure blockchain-based systems.

A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

Detecting abnormal behaviors in smart contracts using opcode sequences

With the fast growth of blockchain technology, blockchain as a decentralized distributed ledger technology has become more widely used and is gradually changing our way of life. But it also raises more and more security issues. As there are more and more smart contracts on the blockchain, and smart contracts cannot be changed once they are added to the blockchain, there is an opportunity for hackers to attack smart contracts. If not handled properly, it will cause serious economic losses to users. In this paper, we introduce a unique method for identifying abnormal behaviors of smart contract vulnerabilities using opcode sequences. We aim to identify the control flow paths triggered by transactions to capture the abnormal behaviors of smart contracts. The control flow paths are the traces on which the transaction is executed. Using Geth instrumentation, we collect the opcode sequences executed on the traces to represent the control flow paths. It should be noted that the process of detecting abnormal behaviors introduces some additional time overhead. However, our experimental results show that this method achieves high abnormal detection accuracy with minimal overhead. This suggests that our proposed method is effective in identifying potential security issues in smart contracts without significantly impacting the overall execution time.

SafeCheck: Detecting smart contract vulnerabilities based on static program analysis methods

AbstractEthereum smart contracts are a special type of computer programs. Once deployed on the blockchain, they cannot be modified. This presents a significant challenge to the security of smart contracts. Previous research has proposed static and dynamic detection tools to identify vulnerabilities in smart contracts. These tools check contract vulnerabilities based on predefined rules, and the accuracy of detection strongly depends on the design of the rules. However, the constant emergence of new vulnerability types and strategies for vulnerability protection leads to numerous false positives and false negatives by tools. To address this problem, we analyze the characteristics of vulnerabilities in smart contracts and the corresponding protection strategies. We convert the contracts' bytecode into an intermediate representation to extract semantic information of the contracts. Based on this semantic information, we establish a set of detection rules based on semantic facts and implement a vulnerability detection tool SafeCheck using static program analysis methods. The tool is used to detect six common types of vulnerabilities in smart contracts. We have extensively evaluated SafeCheck on real Ethereum smart contracts and compared it to other tools. The experimental results show that SafeCheck performs better in smart contract vulnerability detection compared to other typical tools, with a high F‐measure (up to 83.1%) for its entire dataset.

Detecting unknown vulnerabilities in smart contracts using opcode sequences

Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.

A Survey of Ethereum Smart Contract Security: Attacks and Detection

A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This paper is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.

SGuard+ : Machine Learning Guided Rule-based Automated Vulnerability Repair on Smart Contracts.

Smart contracts are becoming appealing targets for hackers because of the vast amount of cryptocurrencies under their control. Asset loss due to the exploitation of smart contract codes has increased significantly in recent years. To guarantee that smart contracts are vulnerability-free, there are many works to detect the vulnerabilities of smart contracts, but only a few vulnerability repair works have been proposed. Repairing smart contract vulnerabilities at the source code level is attractive as it is transparent to users, whereas existing repair tools, such as SCRepair and sGuard , suffer from many limitations: (1) ignoring the code of vulnerability prevention; (2) possibly applying the repair to the wrong statements and changing the original business logic of smart contracts; (3) showing poor performance in terms of time and gas overhead. In this work, we propose machine learning guided rule-based automated vulnerability repair on smart contracts to improve the effectiveness and efficiency of sGuard . To address the limitations mentioned above, we design the features that characterize both the symptoms of vulnerabilities and the methods of vulnerability prevention to learn various vulnerability patterns and reduce false positives. Additionally, a fine-grained localization algorithm is designed by traversing the nodes of the abstract syntax tree, and we refine and extend the repair rules of sGuard to preserve the original business logic of smart contracts and support new vulnerability types. Our tool, named sGuard+ , reduces time overhead based on machine learning models, and reduces gas overhead by fewer code changes and precise patching. In our experiment, we collect a publicly available vulnerability dataset from CVE, SWC and SmartBugs Curated as a ground truth for evaluations. Overall, sGuard+ repairs more vulnerabilities with less time and gas overhead than state-of-the-art tools. Furthermore, we reproduce about 9,000 historical transactions for regression testing. It is shown that sGuard+ has no impact on the original business logic of smart contracts.

DA-GNN: A smart contract vulnerability detection method based on Dual Attention Graph Neural Network

A smart contract is an automated computer program based on blockchain technology. In recent years, the security incidents of smart contracts have caused serious economic losses. However, existing smart contract vulnerability detection methods rely on fixed expert rules, resulting in reduced detection accuracy and scalability. Therefore, addressing the issues of low accuracy in traditional smart contract vulnerability detection methods and the insufficient feature extraction in neural network-based approaches for smart contracts, this paper introduces an intelligent contract vulnerability identification method, Dual Attention Graph Neural Network (DA-GNN). Firstly, DA-GNN transforms the operation code sequence of nodes in the smart contract Control Flow Graph (CFG) into a feature matrix of semantic features and relationships between nodes based on the five types of instructions we propose. Secondly, our proposed dual attention mechanism introduces node semantic features and relationship features between nodes into the GAT to achieve node embedding updates. The updated graph node information is fused through self-attention mechanism to obtain the graph features. Then, the classification and prediction of vulnerabilities are achieved through the classification module. Finally, we evaluated our method on 17,670 real smart contracts. The experimental results show that the precision in detecting integer overflow vulnerabilities, self-destruct vulnerabilities, and transaction sequence dependency vulnerabilities reaches 72.17%, 67.03%, and 73.66%, respectively.

Smart Contract Vulnerability Detection Based on Multi-Scale Encoders

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, to solve the limitations of current deep learning-based vulnerability detection methods in extracting various code critical features, using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-Scale Encoder Vulnerability Detection (MEVD) approach to hit well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model’s attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.

CrossFuzz: Cross-contract fuzzing for smart contract vulnerability detection

Context:Smart contracts are computer programs that run on a blockchain. As the functions implemented by smart contracts become increasingly complex, the number of cross-contract interactions within them also rises. Consequently, the combinatorial explosion of transaction sequences poses a significant challenge for smart contract security vulnerability detection. Existing static analysis-based methods for detecting cross-contract vulnerabilities suffer from high false-positive rates and cannot generate test cases, while fuzz testing-based methods exhibit low code coverage and may not accurately detect security vulnerabilities. Objective:The goal of this paper is to address the above limitations and efficiently detect cross-contract vulnerabilities. To achieve this goal, we present CrossFuzz, a fuzz testing-based method for detecting cross-contract vulnerabilities. Method:First, CrossFuzz generates parameters of constructors by tracing data propagation paths. Then, it collects inter-contract data flow information. Finally, CrossFuzz optimizes mutation strategies for transaction sequences based on inter-contract data flow information to improve the performance of fuzz testing. Results:We implemented CrossFuzz, which is an extension of ConFuzzius, and conducted experiments on a real-world dataset containing 396 smart contracts. The results show that CrossFuzz outperforms xFuzz, a fuzz testing-based tool optimized for cross-contract vulnerability detection, with a 10.58% increase in bytecode coverage. Furthermore, CrossFuzz detects 1.82 times more security vulnerabilities than ConFuzzius. Conclusion:Our method utilizes data flow information to optimize mutation strategies. It significantly improves the efficiency of fuzz testing for detecting cross-contract vulnerabilities.

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts

The Legal Effect and Application of Smart Contracts under the Administrative Law System

Abstract As an important cornerstone of future digital civilization, smart contracts are being increasingly used in blockchain finance in innovative practice, and whether smart contracts have legal effect has become a question that academia must answer. This paper discusses the legal impact and application of smart contracts under the contract layer in the blockchain architecture, with a vision of an administrative legal system. Based on the operation principle of smart contracts, a method for detecting smart contract vulnerabilities that combines Glove word embedding and the Shapelet-Transform algorithm is proposed to improve data security during the fulfillment process. Finally, the smart contract model constructed in this paper is applied to the supply chain of agricultural products, and the legal effects of the smart contract are analyzed using practical examples. The transaction volume of unqualified agricultural products decreased from 19.4545 to 13.4655, which is lower than the production volume. The credit index of the smart contract model has increased, resulting in a shortened node performance time from 0.25s to 0.2s.

Penetration Testing Analysis: Impact on Smart Contract Market Value

This study focuses on specifically looking at how penetration testing affects the market value of smart contracts. This study uses a web-based IDE to deploy smart contracts, and it performs penetration testing utilizing reentrancy and delegatecall attacks. The targeted smart contract is exposed to potential exploitation as a result of the assaults' successful implementation. This study shows that penetration testing indirectly affects token prices since incorrect parameter selection and successful attacks might cause changes in token prices. The results emphasize how critical it is to find and fix smart contract vulnerabilities in order to reduce risks and potential losses.