This paper explores the transformative impact of blockchain technology and smart contracts on the dynamics of trust within the financial sector. Trust is a cornerstone of financial transactions, traditionally established through centralized intermediaries and legal frameworks. However, the advent of blockchain technology introduces a decentralized, transparent, and tamper-resistant trust mechanism. This study aims to analyze how blockchain and smart contracts redefine financial trust by eliminating reliance on third-party intermediaries and automating trust through programmable agreements. Utilizing a mixed-methods approach, including case studies such as JP Morgan’s Quorum blockchain platform, we examine the practical applications of these technologies and their effects on transactional efficiency, data privacy, and trust realization. Key findings reveal that blockchain significantly reduces transaction costs, enhances transparency, and increases security, paving the way for innovative financial products and services. The paper contributes to the understanding of how decentralized technologies are reshaping the future of financial trust and offers insights for regulators and financial institutions navigating this technological shift.
Love Opeyemi David, Marumo Kgomo, Clinton Aigbavboa
Introduction The traditional procurement system in the construction industry has been plagued by inefficiencies, often serving as a significant obstacle to project delivery. Thus, this study examines the dynamics of adopting smart contracts for project procurement for optimal project success and delivery, with insights and recommendations from the South African Construction Industry. Method The study employed a quantitative research approach utilizing descriptive and inferential statistics of Mean Item Score (MIS) and Exploratory Factor Analysis (EFA) for data analysis, based on a purposive sampling technique. Results The MIS results for the benefit, legal & regulatory constraints, and best practices of smart contracts range between 3.73 - 4.41 values, while the Kaiser-Meyer-Olkin (KMO) values were higher than the recommended 0.6 value for the EFA and Cronbach's Alpha value of 0.969 across the indicators. Discussion The study's findings revealed two categorized benefits of adopting smart contracts: administrative and operational efficiency of project procurement and procurement optimization; two components of legal and regulatory constraints: Transactional and legal encumbrance to smart contract implementation and legal gaps and ambiguity and two best practices: smart contract reliability practices for project procurement and consistent stakeholders’ engagement for smart contract protocol standardization. The study concludes that Smart contracts can transform global project procurement within the construction industry. The study recommends the development of a green paper on smart contract adoption and integrating smart contracts into standard forms of construction contracts.
Akinde Michael Ogunmolu, Emonena Patrick Obrik-Uloho, Oluwaseun Oladeji Olaniyi, Aisha Temitope Arigbabu · 5 authors
This study investigates the systemic propagation of cyber risks between traditional financial institutions (TradFi) and decentralized finance (DeFi) infrastructures, focusing on oracles as critical conduits for contagion. Using publicly available datasets—including MITRE ATT&CK® for Financial Services, the Global Cybersecurity Index (GCI), and the REKT.news exploit archive—the study applies frequency analysis, logistic regression, time-series event studies, and Principal Component Analysis with cluster modeling to quantify institutional vulnerabilities, model breach likelihood, and evaluate governance impacts. Empirical findings show that API interconnectivity and DeFi exposure increase breach probabilities by up to 3.7 times, while countries in Cluster 0, such as Singapore and Estonia, exhibit governance indices 24–28 points above average, correlating with lower systemic risks. Oracle-related incidents triggered over 150% volatility surges in TradFi-linked tokens like USDC and DAI, demonstrating oracles’ role in cross-domain cyber risk transmission. The study recommends harmonizing cybersecurity governance frameworks across centralized and decentralized sectors, mandating periodic audits of oracle infrastructures, and developing integrated real-time threat monitoring systems to contain spillovers. These policy measures, alongside expanded cybersecurity workforce development, are essential to mitigate evolving cross-sector vulnerabilities. By combining rigorous empirical modeling with actionable recommendations, this research offers practical insights for policymakers, regulators, and cybersecurity professionals to strengthen resilience in the increasingly interconnected global financial ecosystem.
Based on the document content, I'll create a comprehensive abstract that captures the key aspects of the research. The research investigates the performance and efficiency of various consumer banking platforms using Grey Relational Analysis (GRA). The study analyzed five distinct banking platforms—Online Banks (Nedbank's), Credit Unions, Peer-to-Peer (P2P) Lending, Fintech Solutions, and Cryptocurrency/Decentralized Finance (Deify)—across four critical dimensions: Customer Satisfaction, Digital Banking and Technology, Financial Products and Services, and Customer Support. The analysis employed normalized data, deviation sequences, and grey relation coefficients to establish comprehensive performance metrics. The findings reveal significant variations in platform effectiveness, with Fintech solutions achieving the highest Grey Relationship Grade (GRG: 0.7387), followed closely by P2P lending (GRG: 0.7064). Traditional platforms like Credit Unions maintained moderate performance (GRG: 0.5674), while Cryptocurrency/Deify (GRG: 0.5117) and Online Banks (GRG: 0.5115) showed considerable room for improvement. The research demonstrates that success in modern banking requires a balanced integration of technological innovation with customer-centric services, rather than excellence in isolated areas. These results hold significant importance for shaping the strategic growth of banking services and guiding the future advancement of financial technology platforms.
This project represents a comprehensive digital transformation initiative designed to revolutionize traditional procurement practices through the development of an advanced Smart Tender Management System that enables vendors to seamlessly access complete tender documentation and specifications through a centralized online platform while facilitating efficient electronic bid submission processes. The system fundamentally addresses the inherent inefficiencies and cost burdens associated with conventional tendering methodologies by significantly minimizing additional operational expenses that traditionally encompass extensive advertising campaigns, physical document printing and distribution, manual handling procedures, and administrative overhead costs that often inflate the overall procurement budget. Through its sophisticated digital architecture, the application establishes stringent timeline management protocols that ensure the evaluation process adheres strictly to predetermined schedules and deadlines, thereby eliminating delays that frequently plague traditional tendering systems and compromise project timelines. The platform accommodates multiple vendor participation by providing a robust infrastructure that supports simultaneous bid submissions from diverse suppliers, contractors, and service providers, each presenting unique proposals with varying technical specifications, pricing structures, and implementation methodologies, from which procurement committees can systematically evaluate and select the most suitable proposals based on predetermined criteria including cost-effectiveness, technical merit, vendor credentials, and alignment with organizational objectives. This systematic approach to vendor selection and proposal evaluation has demonstrated significant potential for enhancing organizational profitability through optimized resource allocation, reduced procurement costs, improved vendor competition, and the selection of high-quality solutions that deliver superior value propositions. Furthermore, the implementation of this digital tendering system contributes substantially to improving the overall operational quality and efficiency of organizations by streamlining bureaucratic processes, reducing human error, enhancing transparency and accountability, facilitating better vendor relationships, and providing comprehensive audit trails that support compliance requirements and regulatory standards. The Smart Tender Management System's integration of advanced technologies, including secure document management, automated workflow processes, real-time communication capabilities, and comprehensive reporting mechanisms, positions it as a transformative solution that fundamentally reshapes how organizations approach procurement activities. In essence, this Smart Tender Management System represents a paradigmatic shift from traditional, paper-based, time-consuming procurement practices toward a modern, efficient, technology-driven approach that provides organizations with a powerful, comprehensive tool to systematically streamline their entire tendering ecosystem, significantly reduce operational and financial risks associated with procurement activities, enhance their competitive positioning in increasingly dynamic market environments, and establish sustainable procurement practices that support long-term organizational growth and success while maintaining the highest standards of transparency, efficiency, and stakeholder satisfaction throughout the entire tender lifecycle management process.
The integration of Artificial Intelligence (AI) into decentralized finance (DeFi) has triggered a paradigm shift in the automation and optimization of financial contracts, particularly within the domain of financial derivatives. Derivatives, including options, futures, swaps, and forwards, are among the most complex financial instruments, requiring accurate pricing, efficient settlement, and continuous risk monitoring. Smart contracts—self-executing agreements coded onto blockchain networks—have emerged as a transformative mechanism to automate these processes. However, conventional smart contracts in DeFi are constrained by inefficiencies in execution logic, gas costs, vulnerability to adversarial trading strategies, and limitations in adapting to real-time market fluctuations. This manuscript investigates AI-driven optimization frameworks for smart contracts in derivatives markets, where machine learning algorithms, reinforcement learning agents, and predictive analytics are employed to dynamically enhance pricing mechanisms, counterparty risk management, and execution efficiency. The study builds on an extensive literature review of DeFi, AI-finance integration, and blockchain automation, proposing an AI-augmented smart contract architecture that enables adaptive fee structures, risk-adjusted margin calls, automated dispute resolution, and latency-sensitive derivatives clearing. A simulation-based methodology was employed, where deep reinforcement learning models interacted with synthetic market data to optimize contract logic in futures and options markets deployed on Ethereum Virtual Machine (EVM)-compatible blockchains. Statistical evaluation revealed that AI-enhanced smart contracts demonstrated 25–40% improvement in transaction throughput, 18–25% reduction in gas costs, 30–35% enhancement in derivative pricing accuracy, and 50% reduction in settlement disputes compared to baseline blockchain contracts. The results highlight that AI-driven optimization is not only feasible but essential for scaling derivatives trading in DeFi to institutional-grade levels. The paper concludes by discussing regulatory implications, computational limitations, adversarial AI threats, and the future trajectory of autonomous financial engineering.
Non-fungible tokens (NFTs) have the potential to serve as fiduciary collateral in Indonesia. As a blockchain-based innovation, NFTs enable the unique representation and transfer of digital asset ownership. Under Indonesia’s Fiduciary Security Law, NFTs qualify as fiduciary collateral objects since they are classified as intangible assets. This study examines copyright protection for NFTs in the context of fiduciary collateral, along with the legal and technical challenges in their implementation. Key obstacles include the lack of specific regulatory frameworks, insufficient blockchain infrastructure, and limited public understanding of NFTs as fiduciary collateral. Consequently, there is a need for comprehensive regulations and the establishment of oversight institutions to ensure transactional legality and security.Such regulatory measures are expected to facilitate the use of NFTs as fiduciary collateral, enhance public trust, and promote the growth of a blockchain-based digital ecosystem in Indonesia.
Jun 6, 2025·Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services
The financial sector is pervaded by high uncertainty and at a constant risk of damage to multiple stakeholders, either voluntarily or involuntarily. The highly unpredictable, multi-stakeholder, and multi-dimensional implications of machine learning assurances in finance have caused regulators around the world to impose strict regulations on their use. The heavy documentation requirements imposed on AI systems primarily aim to increase transparency through collaborative scrutiny of different stakeholders by allowing audits to be performed. This auditability requirement raises additional challenges for the implementation of distributed ledger-based systems and can discourage companies from utilizing the advantages such technologies convey. Nonetheless, operating in a system lacking collaborative transparency can pose even higher risks. Hence, the use of AI systems in finance needs to be adequately scrutinized in a manner that maintains the advantages of decentralization while ensuring the maintenance of internal and external compliance.
This study examines the impact of the Financial Stress Index (FSI) and US Equity Market Uncertainty (EMU) on cryptocurrencies. We analyse the short and long-run impact of FSI on prices of the top five cryptos using the Nonlinear ARDL (NARDL) framework to assess the alternative asset suitability of these cryptocurrencies during different financial market stress events. Our analysis finds a statistically significant impact of FSI and EMU on the cryptocurrency returns for both short-run and long-run. While the impact of FSI is asymmetric in the long run, we find that in the short run, the impact is symmetric. Thus, a rise in the FSI has a larger impact on the returns when compared to a fall in the FSI in the long run. The findings across various subperiods suggest that FSI and EMU affect the returns of cryptos differently. While in some periods, we see that a surge in financial stress leads to an increase in returns for some of the cryptos, for others, it leads to a decrease in returns. This indicates that the investors do not have the same preference for all the cryptos during periods of heightened financial stress and they may not be considered equal safe havens. Our results have clear policy implications for investors, regulators, and policymakers.
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars throughout the blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code reviews and formal verification face limitations in scalability, automation, and adaptability to evolving development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models. This paper analyzes how each technique represents code, processes semantic information, and responds to real world vulnerability classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability, computational overhead, and real time applicability. Lastly, it highlights open challenges and future opportunities for advancing this domain.
We introduce the MoveEVM Weakness Classification (MWC) system -- a dedicated vulnerability taxonomy for smart contracts built with Move and executed in EVM-compatible environments. While Move was originally designed to prevent common security flaws via linear resource types and strict ownership, its integration with EVM bytecode introduces novel hybrid vulnerabilities not captured by existing systems like the SWC registry. Our taxonomy spans 37 categorized vulnerability types (MWC-100 to MWC-136) across six semantic frames, addressing issues such as hybrid gas metering, capability misuse, meta-transaction spoofing, and AI-integrated logic. Through analysis of real-world contracts from Aptos and Sui, we demonstrate that current verification tools often miss these hybrid risks. We also explore how formal methods and LLM-based audit agents can operationalize this classification, enabling scalable, logic-aware smart contract auditing. MWC lays the foundation for more secure and verifiable contracts in next-generation blockchain systems. (Shortened Abstract)
Francesco Salzano, Cosmo Kevin Antenucci, Simone Scalabrino, Giovanni Rosa · 6 authors
Abstract The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances, manually labeled at the line level with vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the efficacy of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78% found vulnerabilities, taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and by conducting the largest empirical evaluation of tools to date.
"Smart contracts" are software documented on block chains under specific circumstances that control the allocation of assets between individuals. In a smart healthcare supply chain, product traceability is a major issue. Two enabling technologies in the smart healthcare supply chain that ensure product traceability and safeguard against data manipulation are block chain and smart contracts. A smart contract workflow must be developed and carried out in a block-chain-based supply chain in accordance with the input data. This paper has an objective function to meet the entire system as a parallel composition of smart contracts and users this paper analyze the behavior of smart contracts and a core language of programs with an essential set primitive. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed.
This comprehensive article examines the transformative impact of cloud computing and artificial intelligence on regulatory compliance and risk management in the financial services sector. It explores how financial institutions are embracing cloud technologies to enhance operational capabilities while navigating an increasingly complex regulatory landscape. The article details how AI-driven solutions are reshaping compliance frameworks through advanced machine learning for fraud detection, natural language processing for regulatory analysis, and enhanced anti-money laundering systems. The article analyzes architectural considerations and implementation strategies for AI-powered compliance frameworks, supported by real-world case studies that demonstrate significant improvements in efficiency and effectiveness. Furthermore, the article investigates emerging technologies poised to further transform regulatory compliance, including federated learning, explainable AI, quantum computing, and solutions for decentralized finance. By examining both the opportunities and challenges of AI-driven compliance, this research provides valuable insights for financial institutions seeking to optimize regulatory compliance while maintaining operational efficiency in cloud environments.
The emergence of Decentralized Finance (DeFi) has introduced a paradigm shift in global financial intermediation, challenging traditional banking systems with transparent, algorithm-driven, and blockchain-based financial services. As institutional investors and banks navigate increasingly complex cross-jurisdictional regulatory environments, DeFi presents an opportunity to reimagine asset securitization through decentralized, programmable frameworks. This paper explores the integration of DeFi protocols into institutional asset securitization, focusing on how smart contracts, tokenization, and decentralized liquidity pools can streamline processes, enhance transparency, and reduce reliance on intermediaries across disparate legal and regulatory jurisdictions. At a broader level, the study outlines the limitations of conventional securitization—such as opacity, time lags, and fragmentation—especially in multinational banking ecosystems. It then narrows in on how DeFi tools like automated market makers (AMMs), decentralized exchanges (DEXs), and overcollateralized lending protocols can be adapted to structure, issue, and trade tokenized asset-backed securities (ABS). Particular attention is paid to the challenges of legal enforceability, compliance, and interoperability between blockchain platforms and regulatory frameworks. Case scenarios and pilot initiatives are analyzed to demonstrate the feasibility of decentralized securitization in cross-border finance, including synthetic credit instruments and on-chain risk analytics. The paper further examines how oracles and compliance layers (e.g., KYC/AML-integrated smart contracts) can reconcile DeFi's permissionless nature with institutional governance standards. The findings support a hybrid finance future, where regulated entities harness DeFi infrastructure for secure, compliant, and efficient asset securitization. Policy recommendations are offered to foster collaboration between regulators, financial institutions, and protocol developers in building trust-minimized, scalable, and cross-jurisdictionally aligned financial ecosystems.
Faris Kamil Hasan Mihna, Hazim Akram Sallal, Lobna Abdalhusen Easa Al-Seedi, Hasan Ali Al- Tameemi · 7 authors
The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, plot location, transaction value, and contract duration. Preprocessing of data included encoding categorical data, standardization of numerical variables, and UTF-8 encoded text to preserve data quality. Two classification models were used: Logistic Regression and Random Forest. The model's evaluation used accuracy, precision, recall, and F1-score as evaluation criteria. The Random Forest outperformed with a perfect classification score showing that it may have been better suited to dealing with the complexity and dimensionality of the dataset. The outcomes of the study highlight the role AI could play in automating the analysis of contracts, at the same time highlighting that cybersecurity practices are important when working with data. The framework of this study seeks to support the development of a regulatory regime and add further transparency to real estate contracts in the metaverse - as a scalable tool for future digital real estate management.
The Engineering, Procurement, and Construction (EPC) industry faces significant financial management challenges due to the complexity of project financing, milestone-based payments, and multi-stakeholder collaboration. Traditional on-premise ERP financial systems are often inefficient, leading to delays in financial reporting, security vulnerabilities, and regulatory compliance difficulties. This study explores the development of cloud-based financial solutions tailored to the EPC industry, examining the benefits, challenges, and applicability of existing models such as Software as a Service (SaaS), Platform as a Service (PaaS), and Blockchain-based decentralized finance (DeFi). A Hybrid Cloud-Based Financial Framework is proposed, integrating SaaS for accounting, PaaS for customization, and Blockchain for secure transactions. Experimental validation demonstrates that cloud adoption reduces financial processing time by 87.5%, enhances cash flow visibility, improves security, and increases regulatory compliance efficiency by 40%. This paper highlights the importance of AI-driven predictive analytics, automated compliance, and hybrid cloud models in modern EPC finance and proposes strategies for overcoming integration challenges, cybersecurity risks, and workforce adoption barriers. Future research should focus on scaling hybrid cloud solutions globally and integrating AI-powered risk assessment tools.
The rapid evolution of blockchain technology and decentralized finance (DeFi) has significantly disrupted traditional financial services globally. DeFi, by leveraging blockchain, enables financial services without relying on traditional intermediaries such as banks, creating a more inclusive, efficient, and transparent financial ecosystem. The chapter explores blockchain and DeFi's impact on the Indian financial services sector. The purpose is to identify how these technologies are transforming financial products, services, and regulations in India while addressing key issues like financial inclusion, security, and scalability. The research methodology involves a qualitative approach, including an analysis of secondary data, case studies of Indian blockchain startups. The study will also provide insights into the regulatory and institutional changes required to support this transformation. In conclusion, while blockchain and DeFi offer significant promise for the Indian financial sector, their adoption requires overcoming technological, regulatory, and cultural barriers.
With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.
Tahrim Hossain, Sheikh Hassan, Faisal Haque Bappy, Muhammad Nur Yanhaona · 6 authors
The emergence of blockchain technology has revolutionized contract execution through the introduction of smart contracts. Ethereum, the leading blockchain platform, leverages smart contracts to power decentralized applications (DApps), enabling transparent and self-executing systems across various domains. While the immutability of smart contracts enhances security and trust, it also poses significant challenges for updates, defect resolution, and adaptation to changing requirements. Existing upgrade mechanisms are complex, resource-intensive, and costly in terms of gas consumption, often compromising security and limiting practical adoption. To address these challenges, we propose FlexiContracts+, a novel scheme that reimagines smart contracts by enabling secure, in-place upgrades on Ethereum while preserving historical data without relying on multiple contracts or extensive pre-deployment planning. FlexiContracts+ enhances security, simplifies development, reduces engineering overhead, and supports adaptable, expandable smart contracts. Comprehensive testing demonstrates that FlexiContracts+ achieves a practical balance between immutability and flexibility, advancing the capabilities of smart contract systems.
The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participants’ funds to undetected security flaws. In this work we investigated the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts revealed that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.
Blockchain technology is revolutionizing various industries through decentralized architecture and secure transaction mechanisms, yet its core application—smart contracts—faces increasingly sophisticated security threats. Recognizing the critical need for enhanced protection in this emerging domain, this paper introduces TPH-Fuzz, a two-phase hybrid fuzzing framework designed to overcome current limitations in vulnerability detection. TPH-Fuzz combines global exploration with local vulnerability targeting. It utilizes dynamic symbolic execution for semantics-aware path analysis and employs data-dependency-based state modeling to generate effective transaction sequences. These methods improve both path exploration and vulnerability detection precision significantly. Experiments on a coverage dataset of 9309 contracts demonstrate an 85% branch coverage on complex contracts, outperforming conventional methods; meanwhile, tests on a vulnerability dataset of 1086 labeled contracts show a detection precision of 89.24% across eight vulnerability categories. The promising results underscore the framework’s potential to transform security auditing practices in the blockchain industry, paving the way for more reliable smart contract development and deployment.
Ilham Qasse, Isra M. Ali, Nafisa Ahmed, Mohammad Hamdaqa · 5 authors
The immutability of smart contracts on blockchain platforms like Ethereum promotes security and trustworthiness but presents challenges for updates, bug fixes, or adding new features post-deployment. These limitations can lead to vulnerabilities and outdated functionality, impeding the evolution and maintenance of decentralized applications. Despite various upgrade mechanisms proposed in academic research and industry, a comprehensive analysis of their trade-offs and practical implications is lacking. This study aims to systematically identify, classify, and evaluate existing smart contract upgrade mechanisms, bridging the gap between theoretical concepts and practical implementations. It introduces standardized terminology and evaluates the trade-offs of different approaches using software quality attributes. We conducted a Multivocal Literature Review (MLR) to analyze upgrade mechanisms from both academic research and industry practice. We first establish a unified definition of smart contract upgradeability and identify core components essential for understanding the upgrade process. Based on this definition, we classify existing methods into full upgrade and partial upgrade approaches, introducing standardized terminology to harmonize the diverse terms used in the literature. We then characterize each approach and assess its benefits and limitations using software quality attributes such as complexity, flexibility, security, and usability. The analysis highlights significant trade-offs among upgrade mechanisms, providing valuable insights into the benefits and limitations of each approach. These findings guide developers and researchers in selecting mechanisms tailored to specific project requirements.