Fabric Smart Contract Read-After-Write Risk Detection Method Based on Key Methods and Call Chains

With the wide application of Internet of Things and blockchain, research on smart contracts has received increased attention, and security threat detection for smart contracts is one of the main focuses. This article first introduces the common security vulnerabilities in blockchain smart contracts, and then classifies the vulnerabilities detection tools for smart contracts into six categories according to the different detection methods: 1) formal verification method; 2) symbol execution method; 3) fuzzy testing method; 4) intermediate representation method; 5) stain analysis method; and 6) deep learning method. We test 27 detection tools and analyze them from several perspectives, including the capability of detecting a smart contract version. Finally, it is concluded that most of the current vulnerability detection tools can only detect vulnerabilities in a single and old version of smart contracts. Although the deep learning method detects fewer types of smart contract vulnerabilities, it has higher detection accuracy and efficiency. Therefore, the combination of static detection methods, such as deep learning method and dynamic detection methods, including the fuzzy testing method to detect more types of vulnerabilities in multi-version smart contracts to achieve higher accuracy is a direction worthy of research in the future.

Smart contracts are programs that run on blockchains. The whole smart contract ecosystem tends to be highly homogeneous, due to the immutable nature of contracts once deployed, as well as the copy-paste practice in developing smart contracts. Thus, similarity detection between smart contracts is of great value, which facilitates the quality assurance of the whole ecosystem, by providing a way to identify and track clones among the smart contracts. To this end, this work presents SoliSim, which encodes smart contracts into informative semantic vectors for effective and efficient similarity detection. The smart contract encoding procedure is enforced with masked language modelling on the Solidity programming language, which pre-trains a bert-like model by feeding in normalized token sequences extracted from the smart contracts’ abstract syntax trees (ASTs); while the similarity detection procedure is enforced via simply calculating a score on the encoded numerical vectors. As the experimental results show, the pre-trained strategy adopted by SoliSim is capable of capturing the contextual and semantic information of smart contracts’ code. The similarity scores calculated with SoliSim on pairs of real cloned contracts all exceed 96%, while the values between non-clone pairs are all below 50%.KeywordsSmart contractSimilarity detectionPre-trained model

A smart contract is a computer program which is automatically executed with some conditional statements such as “if/then”. Since smart contracts can include some vulnerable program codes, smart contract exploit was recently highlighted as one of the severe threats to Ethereum blockchain. As one of the efficient and effective smart contract vulnerability detection methods, deep learning methods have been studied due to the fast detection speed and the high detection accuracy. Recently, the deep learning methods using convolutional neural network(CNN) have actively studied to classify images transformed from smart contracts into vulnerable or invulnerable. However, while simply transforming a smart contract into an image and analyzing, semantics and context of the smart contract are ignored to cause false detection alarms. To detect vulnerable smart contracts while maintaining their semantics and context, we propose a new code-targeted CNN architecture, called CodeNet. To improve the performance of CodeNet, we also design a data pre-processing procedure, where a smart contract is transformed into an image while maintaining locality. From the experimental results under various types of vulnerabilities, the proposed CodeNet-based vulnerability detection method shows the good-enough detection performance and detection time compared to well-known state-of-the-art vulnerability detection tools.

Smart contracts on the blockchain – A bibliometric analysis and review

Smart contracts are computer programs running on blockchain platforms to manage large sums of cryptocurrency, govern and carry out transactions of assets between multiple parties. The security of smart contracts has attracted great attention, ever since their adoption in the management of massive cryptocurrency transactions. However, current works mainly rely on the priori security patterns, e.g., reentrancy, delegatecall, gasless send and tainted owner. Although these priori security patterns have been proven effective in finding smart contract vulnerabilities, they are very limited in finding deeper design flaws such as permission bugs, bookkeeping bugs, etc. Moreover, the reliability analysis of smart contracts becomes difficult since most smart contracts have little to no documentation for specifications. Fairness is also an important quality attribute of smart contracts and fairness issues have been proved widely existing in smart contracts such as Ponzi schemes. However, fairness has not yet attracted much research attention. Most security and reliability issues can be detected via conformance checking between software implementation and its specification. As dynamic specification mining has been proven an effective way to learn specifications from software execution traces, in this dissertation, we aim to address the aforementioned challenges by proposing a systematic approach to study the security and reliability of smart contracts based on the learned specifications through dynamic specification mining techniques. In general, contract specifications can be categorized into function-level and contract level specifications. This thesis presents the implementation of InvCon, a dynamic invariant detector for Solidity smart contracts. InvCon analyzes past transac tion histories of a contract to generate likely program invariants, which represent function-level properties that must be maintained during function execution. By combining these invariants with finite event traces from historical transactions, a novel counterexample-guided abstraction refinement algorithm is proposed and implemented in SmCon to mine compact and precise contract-level automata. Additionally, the thesis also presents SPCon that mines the role structures of smart contracts from the past transaction histories for the application of permission bug detection. The thesis demonstrates the applications of program invariants in seman tic bug detection and automated fairness verification. An invariant-based semantic test oracle, implemented in a grey-box fuzzing tool ContraMaster, is developed to enhance vulnerability detection in smart contracts, effectively addressing issues like reentrancy, exception disorder, gasless send, and integer overflow/underflow. For fairness issues, a verification framework FairCon is proposed that treats smart contracts as games and focuses on four fairness properties derived from mechanism design and game theory. Mechanism models are employed for bounded fairness property checking, while program invariants are used to analyze unbounded cases. The thesis also explores the applications of contract-level models in model-based testing and access control bug detection. ModCon is introduced as a tool that allows users to provide test models for smart contracts, specifying state definitions, transition relations, pre/post conditions, invariants, and mapping to the contract code. SPCon leverages the aforementioned role mining to identify permission bugs in smart contracts through conformance testing. The evaluation results demonstrate the high accuracy of permission bug detection, uncovering 11 previously unknown bugs in a well-known smart contract benchmark. Briefly, we have successfully derived various smart contract specifications with dy namic specification mining and studied their applications on security and reliability of smart contracts

As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.

With the widespread use of blockchain, more and more smart contracts are being deployed, and their internal logic is getting more and more sophisticated. Due to the large false positive rate and low detection accuracy of most current detection methods, which heavily rely on already established detection criteria, certain smart contracts additionally call for human secondary detection, resulting in low detection efficiency. In this study, we propose HGAT, a hierarchical graph attention network-based detection model, in order to address the aforementioned issues as well as the shortcomings of current smart contract vulnerability detection approaches. First, using Syntax Tree (AST) and Control Flow Graph, the functions in the smart contract are abstracted into code graphs (CFG). Then abstract each node in the code subgraph, extract the node features, utilize the graph attention mechanism GAT, splice the obtained vectors to form the features of each line of statements and use these features to detect smart contracts. To create test data and assess HGAT, we leverage the open-source smart contract vulnerability sample dataset. The findings of the experiment indicate that this method can identify smart contract vulnerabilities more quickly and precisely than other detection techniques.

The blockchain 2.0 age, marked by smart contract and Ethereum, has arrived couple years ago. Its technologies have expanded the application scenarios of blockchain technology and driven the boom of decentralized Finance. However, smart contract vulnerabilities and security issues are also emerging one after another. Hackers have exploited these vulnerabilities to cause huge economic losses. In recent years, a large amount of research on the analysis and detection of smart contract vulnerabilities has emerged, but there has been no common detection tool and corresponding test dataset. In this paper, we build GSVD dataset (Generalized Smart Contract Vulnerability Dataset) consisting four offline datasets using smart contracts on two chains, Polygon and BSC: two small Solidity datasets consisting of 153 labeled smart contract source codes, which can be used to test the performance of vulnerability mining tools; two large Solidity datasets consisting of 52,202 un labeled real smart contract source codes that can be used to verify the correctness of various theories and tools under a large number of real data conditions. At the same time, this paper integrates the scripting framework accompanying the GSVD dataset, which can execute a variety of popular automated vulnerability detection tools on top of these datasets and generate analysis results of contracts and potential vulnerabilities. We tested the Minor dataset under GSVD using three tools (Slither, Manticore, Mythril) that are kept up to date and found that the combined use of all tools detected 61.1% of labeled vulnerabilities, of which Mythril has the highest detection rate of 42.6%. It is not difficult to conclude that there`re still ample room for advancement for current smart contract vulnerability mining tools because of their underlying methods. Besides, our dataset can contribute to the ultimate target greatly by providing mining tools plenty real contracts information.

SVScanner: Detecting smart contract vulnerabilities via deep semantic extraction

Smart contracts are programs that automatically execute on the blockchain system such as Ethereum. Everybody can write and deploy smart contracts on Ethereum, which causes a large collection of similar contracts via code reuse. In practice, code reuse in smart contract may amplify severe threats like security attacks, resource waste, etc. In this paper, we conduct an empirical study of code reuse in smart contracts for understanding the code reuse practice in the smart contract ecosystem. We first collect 146,452 open-source smart contract projects from Ethereum and then perform a detailed analysis. We first study how often the smart contract projects reuse and then we identify the top reused smart contracts and analyze how the developers revise smart contracts during reuse. Our research suggests that the code reuse in smart contract is quite frequent because about 26% contract code blocks are reused and the average time of reuse is 14.6. And the top reused contracts are almost all related to ERC20 token, which reveals that the current smart contract ecosystem is relatively homogenous. At last, we summarize 7 common types of code revision in smart contracts.

Blockchain is deeply integrated into the vertical industry, and gradually forms an application ecosphere of blockchain in various industries. However, the security incidents of blockchain occur frequently, and especially smart contracts have become the badly-disastered area. So avoiding security incidents caused by smart contracts has become an essential topic for blockchain developing. Up to now, there is not generic method for the security auditing of smart contracts and most researchers have to use existing vulnerability detection technology. To reduce the high false rate of smart contract vulnerability detection, we use ReJection, a detection method based on abstract syntax tree (AST), to focus on the reentrancy vulnerability with obvious harm and features in smart contracts. ReJection consists of four steps. Firstly, ReJection obtains the AST corresponding to the contract by the smart contract compiler solc. Then, AST is preprocessed to eliminate redundant information. Thirdly, ReJection traverses the nodes of the AST and records the notations related to reentrancy vulnerabilities during the traversal, such as Danger-Transfer function, Checks-Effects-Interactions pattern and mutex mechanism. Finally, ReJection uses record information and predefined rules to determine whether the reentrancy vulnerability is occurred. ReJection is implemented based on Slither, which is an open-source smart contract vulnerability detection tool. Furthermore, we also use the open-source smart contract code as the test program to compare experimental results to verify the effects with the ReJection and Slither. The result highlights that the ReJection has higher detection accuracy for reentrancy vulnerability.

Blockchain has attracted widespread attention since its inception and one of the special technologies is smart contracts. Smart contracts are programs on blockchain that act as trusted intermediary between the users and are widely used in variety of industry (e.g., IoT, supply chain management). Smart contracts can store or manipulate valuable assets which may cause huge economic losses. Unlike traditional computer programs, the code of a smart contract cannot be modified after it is deployed on the blockchain. Hence, the security analysis and vulnerability detection of the smart contract must be performed before its deployment. In this survey, we considered 15 security vulnerabilities in smart contracts and introduced the vulnerable areas and the causes of vulnerabilities. According to the methods used, we introduced the existing smart contract analysis methods and vulnerability detection tools from three aspects of static analysis, dynamic analysis and formal verification. Finally, by considering the analysis tools and security vulnerabilities, we found that a new attack cannot be detected by existing detection tools if the vulnerability without pre-defined. We recommend using machine learning methods to analyze smart contracts in combination with traditional program vulnerabilities, and find vulnerabilities that have not yet been discovered in smart contracts. In addition, many detection tools require too much resources or are too complex, so it is necessary to introduce new detection methods.KeywordsEthereumSmart contractsAnalysis toolsVulnerability detection

Smart contracts are programs stored on blockchains to execute transactions. When input constraints or security properties are violated at runtime, the transaction being executed by a smart contract needs to be reverted to avoid undesirable consequences. On Ethereum, the most popular blockchain that supports smart contracts, developers can choose among three transaction-reverting statements (i.e., require, if…revert, and if…throw) to handle anomalous transactions. While these transaction-reverting statements are vital for preventing smart contracts from exhibiting abnormal behaviors or suffering malicious attacks, there is limited understanding of how they are used in practice. In this work, we perform the first empirical study to characterize transaction-reverting statements in Ethereum smart contracts. We measured the prevalence of these statements in 3,866 verified smart contracts from popular dapps and built a taxonomy of their purposes via manually analyzing 557 transaction-reverting statements. We also compared template contracts and their corresponding custom contracts to understand how developers customize the use of transaction-reverting statements. Finally, we analyzed the security impact of transaction-reverting statements by removing them from smart contracts and comparing the mutated contracts against the original ones. Our study led to important findings. For example, we found that transaction-reverting statements are commonly used to perform seven types of authority verifications or validity checks, and missing such statements may compromise the security of smart contracts. We also found that current smart contract security analyzers cannot effectively handle transaction-reverting statements when detecting security vulnerabilities. Our findings can shed light on further research in the broad area of smart contract quality assurance and provide practical guidance to smart contract developers on the appropriate use of transaction-reverting statements.

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and costefficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers.

Blockchain-based smart contracts have received a lot of attention recently. Smart contracts are increasingly being used in a variety of fields, and are touted for their ability to reduce costs, ensure contract fulfillment, and provide high security. This has led to the view that smart contracts can replace existing contracts. However, there is also a negative view that smart contracts are unlikely to replace existing contracts and will only be used in limited areas, citing the limitations of the code itself, the existence of bugs, and the possibility of hacking. These conflicting views reflect the fact that the smart contract debate is far from settled and many issues, such as the legal nature of smart contracts and the legal issues that have arisen or may arise from smart contracts, are still unclear. In this article, we will discuss the legal regulation of smart contracts, among other issues related to smart contracts. Recently, several countries, including some states in the U.S., have moved to incorporate smart contracts into their legal systems, which seems to be a way to provide a legal basis for the future use of smart contracts and the development of related technologies, as well as to regulate them through legislation. The need for legal regulation of smart contracts can be seen in the need to introduce the concept of cryptocurrencies or smart contracts, and to prepare for the possibility that transactions using smart contracts will be activated in the near future. The direction of legal regulation can be broadly categorized into enacting a basic law on smart contracts in the form of a single law, and amending individual laws related to smart contracts. This article opts for the enactment of a basic law on smart contracts in the form of a single law, and discusses factors to be considered when enacting a so-called basic law on smart contracts, as well as what a specific law should look like.