Jishnu Patlola, Rishitha Manyam, Himakar Chappidi, K Suvarchala
This paper investigates the application of CodeBERT, a pre-trained transformer model, to improve the detection of vulnerabilities in smart contracts.Smart contracts, while central to blockchain technology, are susceptible to security flaws that can result in significant financial and operational risks.By fine-tuning CodeBERT on labeled datasets specifically curated for smart contracts, our approach enhances the precision and efficiency of identifying various security issues.This method not only offers a robust solution to the existing challenges in blockchain security but also contributes to the broader efforts to secure decentralized systems and ensure the reliability of blockchain applications
This study explores the optimized application of combining blockchain (Blockchain) and artificial intelligence (AI) in the intelligent risk control of decentralized finance (DeFi). Although the decentralization and transparency of DeFi have driven financial innovation, they have also introduced risks related to market manipulation, smart contract vulnerabilities, and liquidity. Traditional centralized risk control approaches struggle to adapt. This research proposes a blockchain+AI-based intelligent risk control framework. Blockchain’s tamper-resistance enhances transaction security, while AI’s intelligent learning capabilities improve risk identification. Experimental results show that this model outperforms traditional solutions in detection accuracy (94.1%), false alarm rate (2.1%), and detection latency (180ms), and it remains robust under high market volatility. The findings suggest that combining blockchain and AI can effectively strengthen DeFi risk control, enhance system transparency and security, and provide theoretical and practical directions for future intelligent and automated risk management.
Jonas Gabriel Borges da Silva, Diógenes José Gusmão Coutinho
O presente trabalho abordou o potencial dos smart contracts como ferramentas para a redução da burocracia, explorando simultaneamente os desafios jurídicos e as considerações cruciais para sua implementação efetiva no Brasil. Inicialmente, destacou-se a capacidade dos smart contracts de automatizar a execução contratual, minimizar a necessidade de intervenção humana e eliminar intermediário, o que poderia simplificar processos e diminuir entraves burocráticos. A transparência proporcionada pelas plataformas blockchain também foi apontada como um fator que contribui para a construção de confiança. No entanto, a implementação generalizada dos smart contracts enfrenta desafios jurídicos significativos. A principal barreira identificada é a ausência de legislação específica no Brasil para regular esses contratos, gerando incertezas sobre sua validade e eficácia. A interpretação da vontade das partes expressa em código, a definição da natureza jurídica dos smart contracts e as questões de jurisdição e lei aplicável em transações descentralizadas foram também levantadas como complexidades a serem resolvidas. Ademais, foram consideradas as implicações práticas da implementação, incluindo a segurança do código, a interoperabilidade entre plataformas e a necessidade de proteção dos direitos dos contratantes em um ambiente de execução automática. A adoção de mecanismos como a regulação dinâmica e os sandboxes regulatórios foi sugerida como uma possível abordagem para acompanhar a evolução tecnológica e criar um ambiente jurídico mais adaptável. Em suma, embora os smart contracts ofereçam um potencial considerável para simplificar processos e reduzir a burocracia, sua adoção bem-sucedida no Brasil depende da superação de desafios jurídicos e da implementação de medidas que assegurem a segurança jurídica e a proteção dos direitos das partes envolvidas.
This paper proposes an integrated fintech–insurtech architecture that embeds insurance directly into logistics and trade workflows using digital networks, distributed ledger technologies, and real-time risk analytics. Drawing on network effects, game theory, and empirical observations from inland transport and cargo insurance markets in India, the paper models strategic interactions among insurers, shippers, transporters, and trade finance providers. The study introduces an ordinal risk classification framework and a self-sustaining quarterly cargo claims cycle designed to stabilize liquidity, optimize premium pricing, and reduce information asymmetry, particularly for MSMEs operating in fragmented supply chains. By repositioning insurance as a continuously embedded financial infrastructure rather than a post-loss settlement mechanism, the framework demonstrates how digitally native insurance systems can improve trust, capital efficiency, and resilience across integrated logistics and trade ecosystems.
Blockchain and smart contracts enable the development of Decentralized Autonomous Systems (DASs), such as Decentralized Autonomous Applications (DAAs) and Decentralized Finance (DeFi). This paper addresses the challenge of automating smart contract generation for DASs by leveraging the Code LLaMA – Instruct model. A dataset of 6,003 human instruction and source code pairs is used for fine-tuning, employing Quantized Low-Rank Adaptation (QLORA) to optimize the model’s seven billion parameters. The study focuses on generating Solidity-based smart contracts for Ethereum, evaluating the model across four scenarios: cryptocurrency token creation, ownership management, DAO wallet whitelisting, and company information contracts. Results indicate that the fine-tuned model successfully generates functional and efficient smart contracts, demonstrating correctness and optimized gas usage.
탈중앙화 금융(일반적으로 Decentralized Finance를 DeFi로 약칭)은 중개, 거래소 또는 은행과 같은 중앙 금융 중개자에 의존하는 대신에 블록체인 기술을 바탕으로 한 스마트 계약을 활용하는 블록체인 기반 금융 형태를 말한다. DeFi는 블록체인에서 금융 기능을 수행하는 DApp(분산형 애플리케이션)을 중심으로 작동된다.BR/ 스마트 계약(Smart Contract)은 블록체인에 다양한 명령어를 넣은 것을 말하며, 컴퓨터에서 작성되고 블록체인에서 자동으로 실행되는 프로그래밍 코드라고 할 수 있다. 블록체인을 기반으로 한 스마트 계약은 해당 계약의 절차가 미리 작성되어 블록체인상에 배포된 코드를 통해 자동으로 실행되며, 블록체인상에서 분산되어 처리된다. 이 경우 신뢰 확인의 절차는 필요하지 않은데 블록체인의 작업증명(proof-of-work)이 이를 대신하기 때문이다.BR/ DeFi에서 ‘익명성(Anonymity)’은 탈중앙화 금융의 중요한 특징 중 하나이다. 블록체인 기술은 거래의 투명성을 보장하지만, 사용자의 개인정보를 보호할 수 있는 방법도 제공한다. DeFi 플랫폼에서는 사용자가 거래를 할 때 반드시 실명을 밝힐 필요가 없으며, 대부분의 거래가 전자지갑 주소를 통해 이루어진다. 이러한 ‘익명성’은 개인의 프라이버시를 보호할 수 있어, 사용자는 자신의 신원이나 거래 내역이 유출될 염려 없이 금융거래를 진행할 수 있다. 또한 정부나 은행의 규제를 피할 수 있는 장점도 있지만, 불법적인 활동으로도 악용될 수 있다는 우려도 함께 존재한다.BR/ 스마트 계약의 ‘책임성’ 문제는 스마트 계약이 코드로 작성되기 때문에 코드의 버그나 취약점이 발생하면 예기치 않은 결과가 발생하고, 코드를 잘못 작성하거나, 해커가 침입하여 사용자에 손해를 입힌 경우 발생한다. 특히 스마트 계약은 계약이 실행되기 전에 오류를 수정할 방법이 없어서 그 책임을 지는 주체가 모호해지는 문제가 있다. 이로 인해 DeFi 프로젝트에서 발생한 문제에 대해 누구에게 책임을 물을 것인가에 대한 논란이 있을 수 있다.BR/ DeFi는 블록체인 바탕의 스마트 계약으로 금융시스템이 구성되는 구조를 띠고 있으며, 블록체인의 분산원장을 기반으로 네트워크에서 자체 실행되는 컴퓨터코드를 사용하여 작성된 계약체결방식으로 운용되므로 기존의 전통적인 계약 개념과는 다소 차이가 있다. 이에 본 연구에서는 디파이의 익명성과 스마트 계약의 책임성 관계를 어떻게 볼 것인가에 대하여 살펴보았다.
The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community.In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data.Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis.Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results.Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts.The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two.By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.
This study provides a bibliometric analysis of the research trends in financial derivatives within the banking and finance literature. By examining citation patterns, co-authorship networks, and keyword co-occurrences, the study identifies key research themes and their evolution over time. The analysis reveals the central role of derivatives in risk management and financial stability, particularly in the wake of financial crises. It highlights the growth of computational techniques in derivatives pricing and risk management, with an increasing focus on advanced models and simulations. The study also explores the emerging influence of blockchain technology and decentralized finance in reshaping the derivatives landscape. The bibliometric map underscores the global nature of financial derivatives research, with significant contributions from the United States, China, and the United Kingdom. The study provides valuable insights for scholars, practitioners, and policymakers, suggesting areas for further research, particularly in regulatory frameworks, pricing models, and the integration of new technologies in the derivatives market.
The article discusses the concept of new financial instruments, known as synthetic assets, which combine traditional finance with blockchain and decentralized finance (DeFi). These synthetic assets are digital tokens that are created artificially using derivatives. They aim to replicate the characteristics of realworld assets, such as stocks, commodities, and currencies, allowing investors to access these assets without owning them directly. These platforms are powered by smart contracts, which enable access to previously inaccessible markets. The author examines the various classifications, operational models, advantages, regulatory challenges, and potential for future growth and integration of synthetic assets into the global financial system. These synthetic assets are classified based on their underlying asset and liquidity/maturity, and their functionality is based on real-time price predictions transmitted through external tools to the blockchain. Key operational principles for synthetic assets include imitating the behavior of their underlying assets, decentralized operation through the use of smart contracts, the use of collateral, often in the form of cryptocurrencies, and mechanisms to increase liquidity. Various strategies, such as the use of derivatives and leverage, are employed in the trading of these assets. The differences between synthetic assets and other financial instruments are discussed. Synthetic assets have several advantages compared to traditional, tokenized, and derivative assets. These include accessibility, improved risk management, partial ownership, lower transaction costs, programmability, and potentially higher liquidity. However, there are also significant risks associated with synthetic assets, such as volatility due to underlying cryptocurrencies, regulatory uncertainty, and the dependence on price forecasts. The author also considers regulatory and law enforcement issues regarding the classification and decentralized nature of these assets.
Abstract Smart contracts and blockchain technology have revolutionized our transactions and interactions with digital systems, yet their vulnerabilities can lead to devastating consequences such as financial losses, data breaches, and compromised system integrity. Existing detection methods, including static analysis, dynamic analysis, and machine learning-based approaches, have their limitations, such as requiring large amounts of labeled data or being computationally expensive. To address these limitations, we propose a novel approach that leverages a One-Class Variational Autoencoder (VAE) with CodeBERT for data pre-processing to detect vulnerabilities in smart contracts. Our approach achieved a higher F1 score (88.93%) compared to the baselines evaluated, even when labeled data is limited. This paper contributes to the development of effective and efficient vulnerability detection methods, ultimately enhancing the security and reliability of smart contracts and blockchain-based systems. By demonstrating superior performance in imbalanced data scenarios, our method offers a practical solution for real-world applications in blockchain security.
This article examines the integration of blockchain technology and smart contracts within financial regulatory systems and their potential to transform traditional compliance frameworks. The distributed and immutable nature of blockchain presents unique opportunities for enhancing regulatory reporting, fraud detection, and compliance monitoring in financial institutions. Through analysis of implementation cases and theoretical frameworks, this article identifies key applications in automated reconciliation, real-time monitoring, and cross-border regulatory coordination. Despite promising benefits in transparency and automation, significant challenges persist in scalability, legacy system integration, and regulatory uncertainty. This article contributes to the growing body of literature on regulatory technology by providing a comprehensive examination of blockchain applications in financial oversight, offering insights for both regulatory bodies and financial institutions navigating this technological transition. The articles suggest that while blockchain implementation requires substantial infrastructure adaptation, its potential to create more efficient, transparent, and secure regulatory systems warrants continued exploration and development.
Financial data analytics has become a critical tool for businesses seeking to drive growth, enhance fraud prevention, and mitigate risks in dynamic markets. By leveraging large datasets, advanced algorithms, and real-time analytics, organizations can make more informed financial decisions, improve operational efficiency, and enhance compliance with regulatory frameworks. This review explores how financial data analytics contributes to business growth by improving revenue forecasting, identifying market trends, and optimizing financial planning. Companies can leverage predictive models and artificial intelligence to gain competitive advantages through better risk assessment and investment decision-making. Fraud prevention is another key area where financial data analytics plays a transformative role. Machine learning algorithms, anomaly detection systems, and real-time transaction monitoring help identify and prevent fraudulent activities before they cause significant financial losses. Businesses and financial institutions can use automated risk-scoring models to strengthen security in banking, payments, and investment transactions. Risk mitigation in financial markets is also enhanced through data analytics. By employing predictive modeling, scenario analysis, and stress testing, businesses can assess potential market fluctuations and develop strategies to minimize financial exposure. Moreover, analytics-driven regulatory compliance mechanisms improve transparency and reporting, ensuring adherence to legal and industry standards. Despite its advantages, financial data analytics faces challenges such as data privacy concerns, integration with legacy systems, and the need for skilled professionals. However, emerging technologies, including blockchain, AI, and decentralized finance (DeFi), present new opportunities for strengthening financial security and business resilience. This review concludes that financial data analytics is a vital asset for modern businesses, offering strategic insights that drive profitability, enhance fraud detection, and strengthen risk management. Companies must continue to invest in data-driven solutions to stay competitive in an increasingly digital financial landscape. Keywords: Financial data, Business growth, Fraud prevention, Markets.
Embedded finance represents a transformative shift in how financial services integrate within non-financial platforms, creating seamless user experiences that eliminate traditional friction points. This comprehensive article explores how companies have leveraged embedded payment infrastructures to create extensive ecosystems that transcend their original business models. The technical infrastructure powering these innovations—including API-first banking, regulatory technology, and microservices architecture—enables real-time processing at scale while maintaining security and compliance. The evolution toward Super Apps demonstrates how financial transactions can become invisible utilities within broader digital experiences, while artificial intelligence enhances these platforms through predictive analytics and conversational interfaces. Despite technical challenges related to data security, scalability, and cross-border complexity, emerging trends including decentralized finance integration, context-aware services, and embedded insurance promise continued innovation in this rapidly developing field
Stefano Ferretti, Gabriele D’Angelo, Vittorio Ghini, Marco B. Tomasone
This study investigates the detection of vulnerabilities in smart contracts using various transformer models and Large Language Model (LLM) systems. We evaluated BERT, CodeBERT, DistilBERT, and the Gemini model, employing techniques such as aggregation of chunks to enhance performance. The results indicate that simple transformers applied to source code generally perform worse than when applied to byte-code. However, the use of aggregation techniques on the source code significantly improved the model performance. We also evaluate the use of meta-classifiers for multimodal data, by stacking multiple transformers working on source code and byte-code. The Random Forest meta-classifier achieved the highest performance but exhibited significant overfitting. The Gemini model demonstrates limited performance, highlighting the necessity of proper training for LLM systems.
The rapid advancement of financial technology (FinTech) has revolutionized the financial sector by integrating artificial intelligence, blockchain, quantum computing, IoT, and digital payments. These innovations enhance efficiency, security, and accessibility while introducing new cybersecurity challenges such as fraud, identity theft, and ransomware attacks. This research explores emerging FinTech trends, cybersecurity risks, and mitigation strategies to ensure a secure and transparent financial ecosystem. Future advancements in AI-driven automation, decentralized finance (DeFi), and quantum encryption will further shape the financial industry's digital transformation.
This study proposes a decentralized framework that merges smart contract based Decentralized Finance (DeFi) protocols and traditional Enterprise Resource Planning (ERP) systems to provide secure, automatic, and verifiable transaction execution. It constructs an additional middleware interface to guarantee interoperability between ERP modules and blockchain networks that utilize smart contracts for procurement, finance, and asset management modules. The system was tested empirically within a hybrid testbed of chains with Ethereum Virtual Machine (EVM) compatibility simulation executing ERP transaction testing on a simulated environment with physical hardware. According to quantitative assessment results, performance increased, achieving a 38% increase in transaction throughput, a 27% decrease in execution costs, increased trust and traceability due to cryptographic audit trails, and improved auditability. The research highlights the potential of DeFi integrated ERP systems for decentralized enterprise finance systems as a scalable secure replacement to centralized enterprise finance systems.
N. Harini, M R Neethu, Abhimanyu Valsarajan, Ala Manas Royal · 6 authors
Smart contracts have become an integral component of blockchain technology, enabling automated and decentralized transactions. However, their increasing adoption has exposed critical security vulnerabilities, with the reentrancy vulnerability being one of the most prominent threats. This vulnerability arises when external calls to other contracts are made before the completion of a transaction, allowing malicious actors to exploit the contract’s state. In this paper, we propose an efficient and novel solution that will be able to detect both reentrancy and infinite loop vulnerabilities, called GCVAT. Our model analyzes the interactions and dependencies among various components of smart contracts through a series of mechanisms. The GCVAT model is a revolutionary hybrid model that is created by combining the capabilities of a GCN and a GAT model. We present a comprehensive evaluation of our GCVAT model through simulations, showcasing its effectiveness in identifying vulnerabilities in a range of smart contracts. The results demonstrate significant improvements in the detection of accuracy and scalability, making our approach a valuable contribution to ongoing efforts to secure smart contracts. Ultimately, this research aims to foster greater trust in blockchain applications by mitigating the risks associated with reentrancy and infinite loop vulnerabilities.
Nha Minh Nguyen, Malik Abu Afifa, Vo Thi Truc Dao, Duong Van Bui · 5 authors
Purpose This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance? Design/methodology/approach The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data. Findings The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance. Practical implications This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices. Originality/value This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context.
In the ever-evolving landscape of Ethereum smart contracts, the specter of vulnerabilities intensified by code reuse presents a significant challenge to the security of the blockchain. Recent studies employ deep learning for similarity analysis to identify these vulnerabilities, yet their effectiveness wanes as the volume of analyzed code increases. This article introducesSmartTrans, an advanced similarity analysis model designed to efficiently and accurately retrieve similar vulnerabilities within Ethereum bytecodes. Leveraging a novel jump-aware Transformer-based model, our approach captures the semantics and control flow of bytecodes. It not only refines the representation of functions by integrating program analysis with natural language processing techniques but also innovates a contract-level similarity detection scheme tailored for the expansive scale of contracts. Our experiments show thatSmartTransoutperforms state-of-the-art techniques at both function and contract levels, proving its capability to detect n-day vulnerabilities across Ethereum bytecodes accurately. Vulnerabilities recalling experiments show thatSmartTransachieves 95.43% and 99.37% accuracy at two levels. Furthermore, we stand out as the first work to retrieve N-day vulnerabilities across the Ethereum bytecode corpus, unveiling 4,988 vulnerable contracts. Our methodology secures an accuracy of 88.60%, which is 1.30 times higher than the best baseline.
The rapid development of automation and artificial intelligence (AI) is causing a significant upheaval in the banking sector.These technological advancements are boosting client experiences, increasing financial efficiency, and altering the way banks function.With an emphasis on topics like fraud detection, risk management, customer service (think chatbots and virtual assistants), personalized banking, and automating repetitive processes, this study examines how banks are presently utilizing AI and automation.While highlighting the major advantages-such as reducing expenses, reducing mistakes, and expediting decision-making-it also addresses the drawbacks, including concerns about data privacy, maintaining regulatory compliance, and the effect on employment.According to the study, further integration of technologies such as robotic process automation (RPA), machine learning, and natural language processing is anticipated in the future, which will increase the intelligence and adaptability of banking systems.Also, it looks at new developments that have the potential to drastically change the sector, such as open banking, decentralized finance (DeFi), and AI-powered predictive analytics.As the report concludes, banks must carefully consider ethical issues, make investments in staff upskilling, and figure out how humans and computers can collaborate efficiently, even though AI and automation present enormous prospects for innovation and expansion.Although the banking industry has a bright future, maximizing the potential of new technologies will require careful planning.
The non-fungible tokens (NFTs) market has evolved over the past decade, with NFTs serving as unique digital iden-tifiers on a blockchain that certify ownership and authenticity. The trading attributes of NFTs have drawn many users and investors. However, their high value also attracts attackers who exploit vulnerabilities in NFT smart contracts for illegal profits, thereby harming the NFT ecosystem. One notable vulnerability in NFT smart contracts is sleep minting, which allows attackers to illegally transfer others' tokens. Although some research has been conducted on sleepminting, these studies are basically qualitative analyses or based on historical transaction data. There is a lack of understanding from the contract code perspective, which is crucial for identifying such issues and preventing attacks before they occur. To address this gap, in this paper, we categorize the sleep-minting issue and find four distinct types of sleepminting in NFT smart contracts. Each type is accompanied by a comprehensive definition and illustrative code examples to provide a clear understanding of how these vulnerabilities manifest within the contract code. Furthermore, to help detect the defined defects before the sleepminting problem occurrence, we propose a tool named WakeMint, which is built on a symbolic execution framework. WakeMint is designed to be compatible with both high and low versions of Solidity, ensuring broad applicability across various smart contracts. The tool also employs a pruning strategy to shorten the detection period. Additionally, WakeMint gathers some key information, such as the owner of an NFT and emissions of events related to the transfer of the NFT's ownership during symbolic execution. Then, it analyzes the features of the transfer function based on this information so that it can judge the existence of sleepminting. We ran WakeMint on 11,161 real-world NFT smart contracts and evaluated the results. We found 115 instances of sleep minting issues in total, and the precision of our tool is 87.8 %.
In this era, significant transformations in industries and tool utilization are driven by AI/Large Language Models (LLMs) and advancements in Machine Learning. There's a growing emphasis on Machine Learning Operations(MLOps) for managing and deploying these AI models. Concurrently, the imperative for richer smart contracts and on-chain computation is escalating. Our paper introduces an innovative framework that integrates blockchain technology, particularly the Cosmos SDK, to facilitate on-chain AI inferences. This system, built on WebAssembly (WASM), enables interchain communication and deployment of WASM modules executing AI inferences across multiple blockchain nodes. We critically assess the framework from feasibility, scalability, and model security, with a special focus on its portability and engine-model agnostic deployment. The capability to support AI on-chain may enhance and expand the scope of smart contracts, and as a result enable new use cases and applications.