Ziqiao Kong, Xia, Wanxu, Zhengwei Li, Yi Lu · 9 authors
Move is a resource-oriented programming language designed for secure and verifiable smart contract development and has been widely used in managing billions of digital assets in blockchains, such as Sui and Aptos.Move features a strong static type system and explicit resource semantics to enforce safety properties such as the prevention of data races, invalid asset transfers, and entry vulnerabilities. However, smart contracts written in Move may still contain certain vulnerabilities that are beyond the reach of its type system. It is thus essential to validate Move smart contracts. Unfortunately, due to its strong type system, existing smart contract fuzzers are ineffective in producing syntactically or semantically valid transactions to test Move smart contracts. This paper introduces the first fuzzing framework, Belobog, for Move smart contracts. Belobog is type-aware and ensures that all generated and mutated transactions are well-typed. More specifically, for a target Move smart contract, Belobog first constructs a dependency graph based on Move's type system, and then generates or mutates a transaction based on the graph trace derived from the dependency graph. In order to overcome the complex checks in Move smart contracts, we further design and implement a concolic executor in Belobog. We evaluated Belobog on 109 real-world Move smart contract projects. The experimental results show that Belobog is able to detect 100% critical and 79% major vulnerabilities manually audited by human experts. We further selected two recent notorious incidents in the Move ecosystem, i.e., Cetus and Nemo. Belobog successfully reproduced full exploits for both of them, without any prior knowledge. Moreover, we applied Belobog on three ongoing auditing projects and found 2 critical, 2 major, and 3 medium new vulnerabilities, all acknowledged by the project developers.
Xiang, Yuexin, Yang Lei, Yuanzhe Zhang, Qin Wang · 7 authors
Stablecoins such as USDT and USDC aspire to peg stability by coupling issuance controls with reserve attestations. In practice, however, transparency remains fragmented across heterogeneous data sources, with key evidence about circulation, reserves, and disclosure dispersed across records that are difficult to connect and interpret jointly. We introduce a large language model (LLM)-based automated framework for bridging cross-domain transparency in stablecoins by aligning issuer disclosures with observable circulation evidence. First, we propose an integrative framework using LLMs to parse documents, extract salient financial indicators, and semantically align reported statements with corresponding market and issuance metrics. Second, we integrate multi-chain issuance records and disclosure documents within a model context protocol (MCP) framework that standardizes LLM access to both quantitative market data and qualitative disclosure narratives. This framework enables unified retrieval and contextual alignment across heterogeneous stablecoin information sources and facilitates consistent analysis. Third, we demonstrate the capability of LLMs to operate across heterogeneous data domains in blockchain analytics, quantifying discrepancies between reported and observed circulation and examining their implications for transparency and price dynamics. Our findings reveal systematic gaps between disclosed and verifiable data, showing that LLM-assisted analysis enhances cross-domain transparency and supports automated, data-driven auditing in decentralized finance (DeFi).
Blockchain technology is a modern day decentralized databases which presents unique usability changes like “transaction irreversibility, decentralized trust models, and cryptographic key management” which places excessive responsibilities on users that can lead to financial loss and hinders its adoption. Failing to address these usability changes, the current heuristic evaluation models like Nielsen's ten principles need to be revised. Therefore, this paper aims to investigate whether new heuristics are needed to evaluate blockchain applications effectively. During the investigation a critical gap was identified in establishing usability evaluation methods, demonstrating that Nielsen's widely adopted heuristics failed to address 40% of common blockchain-specific usability problems (e.g., users sending funds to incorrect addresses due to poor reversibility awareness). 33 empirical studies were analyzed using systematic review and metaanalysis, mainly focused on blockchain wallets, DeFi (Decentralized Finance) platforms, NFT (Non-Fungible Tokens) marketplaces and Decentralized Autonomous Organizations (DAOs). A set of core usability issues were extracted and thematic analysis was conducted to develop four new blockchain-specific heuristic principles:(1) Transaction Finality Awareness (explicit warnings for irreversible actions), (2) Cryptographic Self-Custody Affordance (intuitive key/seed phrase management), (3) Fee Transparency & Control (realtime gas fee estimation), and (4) Decentralized Value Proposition Clarity. This paper proposes a validation methodology via heuristic evaluation of three blockchain applications by five experts would show a significant increase in detected usability flaws when using the extended heuristics versus Nielsen's original set, projected at approximately 31% based on our gap analysis. Reducing the risk of user errors will require a tailored criteria that will provide experts domain specific heuristics to create a user-friendly Dapp. Thus, this paper not only aims to create a novel domain specific heuristic evaluation for blockchain experts to use as a pre- deployment evaluation tool but also to create safer UX (user experience) for dApps.
Penelitian ini bertujuan untuk menganalisis konsep al-milkiyyah (kepemilikan) terhadap aset digital Non-Fungible Token (NFT) dalam perspektif Ushul Fiqh. Fokus kajian ini adalah menentukan status hukum kepemilikan serta keabsahan transaksi NFT dalam konteks hukum Islam kontemporer. Penelitian ini menggunakan metode hukum normatif dengan pendekatan konseptual, normatif, dan sektoral. Data dikumpulkan melalui studi pustaka terhadap literatur fikih klasik, fatwa ulama kontemporer, serta analisis terhadap karakteristik teknologi blockchain. Data sekunder yang diperoleh dianalisis secara kualitatif dan komparatif untuk mengkaji kesesuaian NFT dengan kriteria harta (mal) dalam fikih muamalah. Hasil penelitian menunjukkan bahwa NFT dapat dikategorikan sebagai harta yang sah secara hukum menurut sebagian ulama kontemporer karena memiliki nilai ekonomi, manfaat yang diakui pasar, dan dapat dimiliki secara eksklusif walaupun tidak berwujud fisik. Namun demikian, keabsahan transaksi NFT bersifat bersyarat. Transaksi dinilai sah apabila objek NFT halal, bebas dari unsur gharar (ketidakjelasan) dan maysir (perjudian), serta menggunakan alat pembayaran yang sesuai prinsip syariah. Penggunaan mata uang kripto yang tidak memenuhi ketentuan syariah dapat membatalkan transaksi. Dengan demikian, NFT dapat diakui sebagai bentuk kepemilikan modern dalam Islam dengan tetap memperhatikan prinsip kehati-hatian, perlindungan harta (hifzh al-māl), dan pencegahan kerusakan (mafsadah). Kata Kunci: Al-milkiyyah; Kepemilikan; NFT; Ushul Fiqh; Fikih Muamalah; Aset Digital
Blockchain is a decentralized distributed ledger technology in which consensus algorithms ensure data consistency and system security among mutually untrusted nodes. The choice of a consensus algorithm significantly affects key performance indicators of a blockchain system, such as transaction throughput and energy consumption. This paper presents a systematic review of blockchain consensus algorithms. Based on their underlying principles, mainstream consensus algorithms are categorized into two major types: proof-based and voting-based mechanisms. Proof-based algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), rely on resource investment to obtain the right to produce blocks; voting-based algorithms, including Raft and Practical Byzantine Fault Tolerance (PBFT), achieve consensus through node collaboration and voting. This paper provides an in-depth analysis of nine representative consensus algorithms and compares them in terms of efficiency, scalability, fault tolerance, and complexity. Finally, the paper summarizes the current challenges and potential research directions in the field, offering theoretical support and reference for the design of future blockchain systems.
Blockchain has become peer-to-peer immutable distributed ledger technology network, and its consensus protocol is essential to the management of decentralized data. The consensus algorithm, at core of blockchain technology (BCT), has direct impact on blockchain's security, stability, decentralization, and many other crucial features. A key problem in development of blockchain applications is selecting the right consensus algorithm for various scenarios. Ensuring scalability is the most significant drawback of BCT. The industry has been rejuvenated and new architectures have been sparked by the usage of consensus protocols for blockchains(BC). Researchers analyzed shortcomings of proof of work (PoW) consensus process and subsequently, alternative protocols like proof of stake (PoS) arose. PoS, together with other improvements, lowers the unimaginably high energy usage of PoW, making it protocol of time. In PoS, only the user with highest stake becomes the validator. To overcome this, we propose Scalable Proof of Stake (SPoS), a novel consensus protocol, which is an enhancement of PoS protocol. In the proposed algorithm, each stakeholder based on the stake gets a chance to become the validator and can mine blocks in the blockchain. Clustering of the stakeholders is done using mean shift algorithm. Each cluster gets a different number of blocks to mine in BC. Cluster with highest stake will get a greater number of blocks to mine when compared to other groups and the cluster with the least stake gets least number of blocks to mine when compared to other groups. To mine the blocks, validator is chosen based on the cluster in which he is present. Fair mining is ensured for all stakeholders based on number of stakes. Mining is distributed among all the stakeholders. Since the validators are chosen fast, the transaction rate is high in the network. Validators in PoS are selected according to the quantity of cryptocurrency they stake. More stakeholders will get chance of validating blocks and receiving rewards. Over time, this reduces fairness and decentralization by concentrating on wealth and power. This is addressed in SPoS using clustering-based validator assignment.
This comprehensive technical specification details the ADN-NChain protocol, a first-of-its-kind Distributed Ledger Technology (DLT). It introduces a revolutionary self-repairing neural blockchain architecture, leveraging biologically-inspired genetic algorithms for state management and unprecedented network resilience to systemic and quantum threats. The whitepaper thoroughly covers the robust system design, the novel Proof-of-Resonance (PoR) consensus mechanism, and a future-proof post-quantum cryptography implementation (CRYSTALS-Dilithium) essential for securing the next generation of crypto-assets and decentralized applications (dApps). Performance metrics confirm high scalability (12,000+ TPS) and exceptional data integrity with a 99.9% automatic corruption repair success rate. This work is critical for researchers, investors, and developers focused on next-generation blockchain, Web3, cryptocurrency security, and Decentralized Finance (DeFi) solutions.
In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.
The global Web3 Payments Market is projected to grow from USD 2.83 billion in 2023 to USD 44.98 billion by 2030, driven by decentralized blockchain adoption and AI integration. Web3 payments enable secure, transparent, and cost-efficient financial transactions for businesses worldwide. To access the full report, visit - https://www.nextmsc.com/report/web3-payments-market
HOT Protocol provides the infrastructure that allows smart contracts to securely own and manage private keys. The Multi-Party Computation (MPC) Network manages signing keys. By running an MPC node inside a Trusted Execution Environment (TEE), the protocol achieves stronger security guarantees while lowering economic requirements for participants. The NEAR Protocol provides a decentralized and efficient state layer. Key management can be integrated with any smart contract across Stellar, TON, Solana, and EVM-compatible networks.
In 2025, the blockchain network Qubic conducted a mining campaign on Monero, a privacy-focused cryptocurrency. It publicly presented the campaign as a ``51\% takeover'' and a demonstration of selfish mining. The episode provides a rare opportunity to test such claims in a privacy-preserving proof-of-work network, where pool attribution and private release decisions are difficult to observe. We combine Monero node measurements, Qubic pool observations, community-shared artifacts, and disclosed view keys to attribute blocks and identify candidate withholding periods. Rather than sustained majority control, we observe elevated orphaning and deeper reorganizations. Qubic did not follow a single optimized selfish mining strategy, instead varying between standard selfish mining and more defensive block-release decisions. Across the campaign, it gained no reward advantage over honest mining. Community monitoring and Qubic's countermeasures reveal how real-world mining campaigns evolve in response to an active ecosystem. Overall, the campaign disrupted Monero without exhibiting a stable profit-maximizing strategy, leaving open disruptive incentives beyond direct mining rewards.
Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. However, decision-making in fully dynamic and live environments remains highly challenging, requiring real-time information integration and adaptive responses. While existing efforts have explored live evaluation mechanisms in structured tasks, a critical gap remains in systematic benchmarking for real-world applications, particularly in finance where stringent requirements exist for live strategic responsiveness. To address this gap, we introduce AI-Trader, the first fully-automated, live, and data-uncontaminated evaluation benchmark for LLM agents in financial decision-making. AI-Trader spans three major financial markets: U.S. stocks, A-shares, and cryptocurrencies, with multiple trading granularities to simulate live financial environments. Our benchmark implements a revolutionary fully autonomous minimal information paradigm where agents receive only essential context and must independently search, verify, and synthesize live market information without human intervention. We evaluate six mainstream LLMs across three markets and multiple trading frequencies. Our analysis reveals striking findings: general intelligence does not automatically translate to effective trading capability, with most agents exhibiting poor returns and weak risk management. We demonstrate that risk control capability determines cross-market robustness, and that AI trading strategies achieve excess returns more readily in highly liquid markets than policy-driven environments. These findings expose critical limitations in current autonomous agents and provide clear directions for future improvements. The code and evaluation data are open-sourced to foster community research: https://github.com/HKUDS/AI-Trader.
In recent years,the security issues of smart contracts have become increasingly prominent,and vulnerability detection has become a key challenge.In scenarios where source code is not publicly available,bytecode-based detection methods have attracted significant attention.However,existing deep learning methods typically rely solely on sequences or graph structures,which makes it difficult to fully capture vulnerability features.To address this,this paper proposes a smart contract bytecode vulnerability detection method based on heterogeneous graphs and instruction sequences,called RGCN-ResNet1D(Relational Graph Convolutional Network and ResNet-based 1D Convolutional Network).This method models bytecode as a heterogeneous graph and instruction sequence,using a Relational Graph Convolutional Network(RGCN) to extract structural features and a ResNet-based 1D Convolutional Network(ResNet1D) to extract sequential features,and then fuses the two types of features for vulnerability detection.A cross-entropy loss function is also designed,which dynamically adjusts the weight based on the number of misclassified samples,effectively alleviating the class imbalance problem in the training set.Experimental results show that RGCN-ResNet1D achieves F1 scores of 95.43%,90.67%,and 92.31% for detecting integer overflow,timestamp dependency,and self-destruct vulnerabilities,respectively,significantly outperforming the comparison methods.
The arrival of quantum computing poses a huge threat to conventional public key cryptography used in blockchain systems such as Bitcoin. To address this challenge, we proposes and evaluates a quantum resistant sidechain framework that integrates post quantum digital signature schemes and quantum key distribution (QKD) protocols with the Bitcoin mainchain. Using a Python simulation environment, the performance of multiple signature algorithms. ECDSA as a baseline measurement, Falcon, CRYSTALS-Dilithium, and SPHINCS+ were analyzed in combination with quantum communication protocols BB84, E91, and SARG04. Experimental results show that ECDSA remains the fastest baseline scheme but lacks quantum resistance, while SPHINCS+ provides the highest security with an expected overhead of 93.5 %. Among quantum protocols, BB84 achieved the best overall efficiency across transaction sizes. The optimal integration pairs were BB84 and CRYSTALS-Dilithium combination for speed, security balance and SPHINCS+ and E91 for maximum quantum resilience. These findings demonstrate the practical feasibility of deploying post quantum cryptographic components in Bitcoin compatible sidechains, paving the way for future blockchain networks that remain secure in the post quantum era.
Hagar M. Mohamed, Mahmoud Khalifa, Amina Toumi, Muna Ali
Major engineering advances synthetic biology, precision genome editing, nanotechnology, advanced biomaterials, scalable manufacturing, and artificial intelligence are converging to enable therapeutic paradigms that could shift oncology from long-term disease control toward durable cures. Yet technical breakthroughs alone are insufficient. Realizing care-driven care at scale requires adaptive regulatory frameworks, outcome-linked financing models, federated data governance, manufacturing policy for decentralized production, and equity-centered implementation. This paper synthesizes the engineering frontier, maps translational bottlenecks, and proposes a practical policy-aware translational ecosystem the Cure-Driven Partnership Model (CDPM) to accelerate safe, ethical, and equitable deployment of curative cancer interventions. We conclude with prioritized research objectives, governance recommendations, and an implementation checklist for stakeholders.
A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Herman Herman, Rinday Zildjiani Salji, Herman Yuliansyah
This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
A fragmented workflow, infrequent payment milestones, and unclear supply chains are some of the continuing problems, which complicate the contract administration in the UAE oil-and-gas construction sector. To examine blockchain and smart contract usage as a way to solve these issues, this paper reviews the literature on the topic, concentrating on the Technology Acceptance Model (TAM) as a framework that can be used to examine its use.The past research indicates that the perceived usefulness (PU) and the perceived ease of use (PEOU) play a significant role in predicting the intention of users to use new technology. They emphasize the need to be transparent and automated. Other barriers that are identified in the literature include regulatory uncertainty, system integration issues, and organizational inertia. Based on these results, we describe the prerequisites and the preconditions of successful adoption. These are regulatory clarity, clear policy frameworks, specific technical training and implementation roadmaps. This paper can provide practical advice to policy-makers in the supply-chain digitization sector and industry executives in the UAE oil-and-gas sector of construction by combining scattered studies.
It is worth noting that the topic of cryptocurrencies is characterized by modernity, and the resulting vacuum exists for many of them, and this is entirely the result of the failure of the vast majority of countries and international organizations to analyze them, to distinguish the topic as virgin, especially since it has been digital, so it pushes modern and innovative has become at the forefront. Details list Controversy around the world, as cryptocurrencies represented a dangerous stage in the development of currencies that we witness today in different eras, especially in light of the noticeable spread of these currencies, whether in the present or in the future One of the most important problems resulting from dealing in cryptocurrencies has become the lack of legislative texts that address disputes arising from the trading of digital currency in most countries and international organizations. We also did not find legal legislation for cryptocurrencies in Iraqi legislation that regulates them, and the issue of Research is considered a virgin topic in private international law. Being a complex and thorny subject that includes technical rules, technical complexities and multiple inter-related relationships involving multiple legal systems, which requires an integrated legal system.
Open access
Blockchain Technology Applications and Security
Security, Politics, and Digital Transformation
Innovations and Analysis in Business and Education
Purpose – The purpose of this research is to explore the opportunities and barriers related to the use of cryptocurrencies in tourism from the local community’s perspective. Cryptocurrencies are increasingly accepted worldwide, yet their use in tourism consumption remains limited. Evaluating the attitudes and readiness of residents in urban areas, particularly in Zagreb, is essential for assessing the sustainability of digital payment technologies in tourism. Methodology – The research was conducted in Zagreb and its surroundings, with a sample of 484 respondents. A structured questionnaire was used to assess knowledge, perceived security, intention to use, and perceived barriers and incentives regarding cryptocurrency usage in tourism. Data analysis involved descriptive statistics and Pearson’s Chi-square test to examine relationships between key variables and sociodemographic factors. Findings – The results indicate limited awareness about cryptocurrencies, with more than 75% of respondents being completely unfamiliar or only superficially familiar with the topic. A small percentage currently uses cryptocurrencies, but there is substantial conditional willingness for future usage, particularly if regulatory, educational, and security issues are addressed. Statistically significant gender differences were observed in perceived awareness and trust in Bitcoin systems, with men exhibiting higher levels of awareness and trust compared to women. Contribution – This study provides valuable insights into local community readiness for cryptocurrency usage in tourism, highlighting the significance of education, trust, and regulatory frameworks. The findings can serve as a foundation for policymakers, tourism stakeholders, and digital innovators to develop strategies for the effective integration of cryptocurrencies into tourism economies.