Mutation testing for integer overflow in ethereum smart contracts

Integer overflow vulnerability is a kind of common software vulnerabilities, there has been no effective way to detect integer overflow vulnerabilities. Because of the lack of dynamic execution, static analysis can not determine the run-time distribution of memory, and may miss the detection of possible security issues; source code auditing is an expensive and time consuming process. Although there has been applying mutation analysis for testing ANSI C programs, and lots of mutation operators have been designed with respect to specific questions, there are not any of operators specifically designed for integer overflow. In this paper, we propose some new mutation operators to force the generation of adequate test data set for integer overflow vulnerabilities. The results indicate that the proposed operators are effective for detecting integer overflow vulnerabilities.

With the rapid development of the blockchain, smart contract technology has been widely applied. The number of smart contracts has grown at a high rate and nearly at an average of thousands per day. However, the correctness and security of the smart contract itself are facing huge problems. The well-known DAO vulnerability, and Parity multi-signature wallet’ vulnerabilities have leaded to a hundreds of millions dollars loss, and they are both caused by the security problems of smart contracts. Once the smart contract vulnerability is exploited, it is very likely to bring the loss of cryptocurrencies, the disorder of the financial order and other catastrophic consequences. Therefore the security of smart contracts is imminent. This project has designed and implemented a vulnerability detection system of Ethereum smart contract. The system uses the assembly instruction sequences of the smart contract to generate the control flow graph, then performs symbolic execution and vulnerability constraint solving over the control flow. The system can detect some common types of vulnerabilities, such as the integer overflow and underflow vulnerability, reentry vulnerability and unchecked call return value vulnerability. It has a high accuracy of detection result, and gives support for export vulnerability report.

Ethereum smart contracts are computer programs that are deployed and executed on the Ethereum blockchain to enforce agreements among untrusting parties. Being the most prominent platform that supports smart contracts, Ethereum has been targeted by many attacks and plagued by security incidents. Consequently, many smart contract vulnerabilities have been discovered in the past decade. To detect and prevent such vulnerabilities, different security analysis tools, including static and dynamic analysis tools, have been created, but their performance decreases drastically when codes to be analyzed are constantly being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool that detects smart contract vulnerabilities. Eth2Vec maintains its robustness against code rewrites; i.e., it can detect vulnerabilities even in rewritten codes. Other machine-learning-based static analysis tools require features, which analysts create manually, as inputs. In contrast, Eth2Vec uses a neural network for language processing to automatically learn the features of vulnerable contracts. In doing so, Eth2Vec can detect vulnerabilities in smart contracts by comparing the similarities between the codes of a target contract and those of the learned contracts. We performed experiments with existing open databases, such as Etherscan, and Eth2Vec was able to outperform a recent model based on support vector machine in terms of well-known metrics, i.e., precision, recall, and F1-score.

Blockchain technology and its applications are gaining popularity day by day. It is a ground-breaking technology that allows users to communicate without the need of a trusted middleman. A smart contract (self-executable code) is deployed on the blockchain and auto executes due to a triggering condition. In a no-trust contracting environment, smart contracts can establish trust among parties. Terms and conditions embedded in smart contracts will be imposed immediately when specified criteria have been fulfilled. Due to this, the malicious assailants have a special interest in smart contracts. Blockchains are immutable means if some transaction is deployed or recorded on the blockchain, it becomes unalterable. Thus, smart contracts must be analyzed to ensure zero security vulnerabilities or flaws before deploying the same on the blockchain because a single vulnerability can lead to the loss of millions. For analyzing the security vulnerabilities of smart contracts, various analysis tools have been developed to create safe and secure smart contracts. This paper presents a systematic review on Ethereum smart contracts analysis tools. Initially, these tools are categorized into static and dynamic analysis tools. Thereafter, different sources code analysis techniques are studied such as taint analysis, symbolic execution, and fuzzing techniques. In total, 86 security analysis tools developed for Ethereum blockchain smart contract are analyzed regardless of tool type and analysis approach. Finally, the paper highlights some challenges and future recommendations in the field of Ethereum smart contracts.

Smart contract (SC) is an extension of BlockChain technology. Ethereum BlockChain was the first to incorporate SC and thus started a new era of crypto-currencies and electronic transactions. Solidity helps to program the SCs. Still, soon after Solidity's emergence in 2014, Solidity-based SCs suffered many attacks that deprived the SC account holders of their precious funds. The main reason for these attacks was the presence of vulnerabilities in SC. This paper discusses SC vulnerabilities and classifies them according to the domain knowledge of the faulty operations. This classification is a source of reminding developers and software engineers that for SC's safety, each SC requires proper testing with effective tools to catch those classes' vulnerabilities.

Permissionless blockchain-enabled smart contracts that use the proof-of-stake (PoS) algorithm for validation of transactions have scalability and on-chain governance as a primary concern. We consider smart contracts that use lite mobile wallets with support of the unspent transaction output protocol (UTXO) and simple payment verification (SPV) to increase the number of transaction throughput. Furthermore, we investigate in this paper to which extent it is possible to change the parameters of the blockchain using smart contracts in hot parameter swaps at runtime for enabling continuous soft forks. The leading smart contract solution Ethereum does not have the functionality to change blockchain parameters and requires hard forks multiple times, and thus, Ethereum smart contracts are not scalable for large industrial applications. This whitepaper fills the gap by presenting the Qtum’s decentralized-governance protocol (DGP) that aims for managing essential blockchain parameters using smart contracts for enabling frequent soft forks. Compared with the Ethereum alternative, we explore the Qtum utility advantages and current Qtum smart contract future development plans integrating dedicated x86 hardware development for industry case applications.KeywordsSmart contractBlockchainMobileLite walletPoSAbstraction layerSVPUTXODecentralized governance

• Created a refined, expanded, and precisely labeled dataset with explanations, risk assessments, and fixes for each vulnerability • Fine-tuned an open-source LLM: LLaMA-3.1-8B using parameter-efficient techniques (LoRA) for smart contract vulnerability detection • Fine-tuned GPT-4o-mini on the same corpus for comparative analysis • Developed a real-time Visual Studio Code (VSCode) plugin integrating GPT-4o-mini for smart contract auditing • Released the datasets, tool, and fine-tuned model to advance research in smart contract security Since the advent of Ethereum, ensuring the security of smart contracts has become imperative. Integer overflow and underflow, reentrancy, and timestamp dependency remain the three most prevalent vulnerabilities in deployed contracts. Existing static-analysis tools often yield insufficient accuracy, and datasets derived from them inherit the same shortcomings. Moreover, the smart contract ecosystem lacks a dependable, real-time auditing aid for developers and a fine-tuned model trained on a truly comprehensive corpus. In this paper, we present three main contributions. (1) Dataset curation: the state-of-the-art vulnerability datasets are aggregated and harmonized, producing a clean, fully labeled dataset that integrates detailed explanations, potential security risks, vulnerable line ranges, code snippets, and corresponding fixes. The dataset is publicly available via our GitHub repository. (2) Model fine-tuning: the LLaMA-3.1-8B model as well as GPT-4o-mini are fine-tuned on this corpus and evaluated with both standard classification metrics and text-quality measures. The fine-tuned LLaMA-3.1 model achieves a precision of 93.55%, an average semantic similarity of 77.48%, and a code similarity of 87.25%. (3) IDE integration: We implement a real-time Visual Studio Code extension, backed by the GPT-4o API, that highlights, explains, and automatically patches vulnerabilities as the developer writes. Together, these contributions deliver a rigorously validated model and a practical developer toolchain that markedly advance the state of smart contract security research and practice.

The inception of blockchain techniques has been revolutionizing various domains, e.g., Internet of Things, supply chain and healthcare. Ethereum smart contracts emerge as the promising blockchain application, which could enable distrustful parties to participate in the automatic and trustful transactions. Given the increasing importance of Ethereum smart contracts, understanding them becomes imperative. However, prior work only studied smart contracts with general high-level patterns, and one critical question has not been answered yet: how do smart contracts behave individually? In this paper, we present a behavior-aware profiling of individual smart contract from a multi-party perspective, which improves the visibility of the smart contract ecosystem. We conduct a detailed study of the behavior of individual smart contract on two real-world datasets, and our profiling reveals interesting and surprising observations. For example, a few contract completion chains have more than 50 contracts and all of them belong to the Finance category. We also discuss the implications that lead to recommendations to improve the security and performance of the smart contract ecosystem. Overall, our work effectively complements previous work towards generating a comprehensive understanding of smart contracts.

In recent years, Ethereum gained tremendously in popularity, growing from a daily transaction average of 10K in January 2016 to an average of 500K in January 2020. Similarly, smart contracts began to carry more value, making them appealing targets for attackers. As a result, they started to become victims of attacks, costing millions of dollars. In response to these attacks, both academia and industry proposed a plethora of tools to scan smart contracts for vulnerabilities before deploying them on the blockchain. However, most of these tools solely focus on detecting vulnerabilities and not attacks, let alone quantifying or tracing the number of stolen assets. In this paper, we present Horus, a framework that empowers the automated detection and investigation of smart contract attacks based on logic-driven and graph-driven analysis of transactions. Horus provides quick means to quantify and trace the flow of stolen assets across the Ethereum blockchain. We perform a large-scale analysis of all the smart contracts deployed on Ethereum until May 2020. We identified 1,888 attacked smart contracts and 8,095 adversarial transactions in the wild. Our investigation shows that the number of attacks did not necessarily decrease over the past few years, but for some vulnerabilities remained constant. Finally, we also demonstrate the practicality of our framework via an in-depth analysis on the recent Uniswap and Lendf.me attacks.KeywordsEthereumSmart contractsAttack detectionForensics

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes. However, these state-of-the-art approaches face several major limitations, including lacking interpretability, high false positive rates and the weak robustness to evasion techniques, These limitations mean that existing real-world methods for detecting Ponzi schemes are ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, we propose a heuristic-guided symbolic execution technique to identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins. In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

The paper aims at creating ERC-20 Token and crowd sale step-by-step with Ethereum smart contracts. In the process, it focuses on testing the smart contracts, deploying the smart contracts to the Ethereum blockchain, and building an ICO website deployed to the web for the buying and selling of the tokens. The Ethereum blockchain provides a platform for creating our cryptocurrency, or tokens that can be purchased via Ether which is the native cryptocurrency of the Ethereum blockchain. ERC-20 is a standard document that specifies the behavior of the tokens so that they are compatible with other platforms like cryptocurrency exchanges. We used Ethereum which is a blockchain like Bitcoin. We created a token called ;Token ; and there are 100,000,000 such tokens. Firstly, the token smart contract keeps track of some token attributes which are basic. It also keeps track of who owns ;My Token ; and how much. ERC-20 tokens can be used as a payment just, just like any other cryptocurrency, from one account to another. They can also be purchased in a crowd sale, like an ICO.

Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis tools have been created to detect such vulnerabilities, but their performance decreases drastically when codes to be analyzed are being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool for vulnerability detection, with robustness against code rewrites in smart contracts. Existing machine-learning-based static analysis tools for vulnerability detection need features, which analysts create manually, as inputs. In contrast, Eth2Vec automatically learns features of vulnerable Ethereum Virtual Machine (EVM) bytecodes with tacit knowledge through a neural network for language processing. Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned. We conducted experiments with existing open databases, such as Etherscan, and our results show that Eth2Vec outperforms the existing work in terms of well-known metrics, i.e., precision, recall, and F1-score. Moreover, Eth2Vec can detect vulnerabilities even in rewritten codes.

BackgroundWearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers.ObjectiveWe aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace.MethodsWe used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system.ResultsWe designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study.ConclusionsA decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.

Smart contracts in Ethereum are executed by the Ethereum Virtual Machine (EVM). We defined EVM in Lem, a language that can be compiled for a few interactive theorem provers. We tested our definition against a standard test suite for Ethereum implementations. Using our definition, we proved some safety properties of Ethereum smart contracts in an interactive theorem prover Isabelle/HOL. To our knowledge, ours is the first formal EVM definition for smart contract verification that implements all instructions. Our definition can serve as a basis for further analysis and generation of Ethereum smart contracts.