This article examines the structural tension between territorially organised sanctions regimes and decentralised crypto and tokenised assets. It argues that the principal challenge lies not in the formal applicability of sanctions, but in an enforcement gap: traditional coercive models are designed around identifiable persons and legally ownable property, while distributed ledger systems operate beyond territorial seizure and technical shutdown. The study advances lex digitalis rei sitae as a functional connecting factor linking proprietary and sanctions-related effects to the jurisdiction exercising effective regulatory control over access and liquidity. It further demonstrates that sanctions enforcement increasingly relies on address-based designations, compliance obligations imposed on regulated infrastructure, and programmable compliance embedded in smart contracts and oracle systems. The article concludes that effective digital sanctions require modernised conflict-of-laws rules, explicit recognition of digital identifiers as sanctionable control points, and compliance mechanisms consistent with rule-of-law safeguards.
Lucas Monteiro de Oliveira, Olívia Brandão Melo Campelo
O presente artigo tem por objetivo analisar os limites jurídicos da autoexecução nos smart contracts, especialmente quando tais instrumentos, estruturados em tecnologia blockchain, produzem efeitos obrigacionais de difícil ou impossível reversão técnica. Parte-se do problema segundo o qual a programação contratual pode executar automaticamente prestações, transferências patrimoniais ou efeitos negociais sem intervenção humana posterior, ao mesmo tempo em que o ordenamento jurídico brasileiro preserva institutos como arrependimento, anulação, resolução, restituição, responsabilidade civil e vedação ao enriquecimento sem causa. A pesquisa, de natureza bibliográfica e qualitativa, examina a compatibilidade entre a lógica algorítmica dos contratos inteligentes e os fundamentos clássicos do Direito Civil e do Direito do Consumidor. Sustenta-se que a irreversibilidade técnica não pode ser convertida em irreversibilidade jurídica, pois a eficácia automática do código não afasta a incidência das normas relativas à validade do negócio jurídico, à boa-fé objetiva, à função social do contrato, à proteção do consumidor e à reparação de danos. Conclui-se que os smart contracts podem ser admitidos no ordenamento brasileiro, desde que sua arquitetura tecnológica permaneça subordinada à normatividade jurídica, mediante mecanismos de reversão, compensação, suspensão, auditoria, governança e responsabilização.
The rapid growth of Real World Asset (RWA) tokenization faces a critical vulnerability: the "Physical Oracle Problem." While blockchain ensures digital immutability, it remains blind to the physical state of the underlying asset (e.g., structural degradation in real estate or hidden damage in naval vessels). This document introduces the Prop Trust Verified Standard (PTVS), a comprehensive forensic methodology designed to bridge this gap. Developed by Aurema Group, PTVS establishes a rigorous protocol for physical asset auditing, combining certified judicial expertise (Perito Judicial) with cryptographic anchoring. The methodology ensures that physical inspections, material verifications, and compliance checks are immutably recorded and linked to smart contracts (e.g., ERC-3643), providing institutional-grade trust for Family Offices, tokenization platforms, and regulatory bodies under frameworks like eIDAS (EU 910/2014). This report outlines the core principles, verification workflows, and case study applications of PTVS in real estate and maritime sectors. Español: El rápido crecimiento de la tokenización de Activos del Mundo Real (RWA) enfrenta una vulnerabilidad crítica: el "Problema del Oráculo Físico". Mientras que la blockchain garantiza la inmutabilidad digital, permanece ciega al estado físico del activo subyacente (ej. degradación estructural en inmuebles o daños ocultos en embarcaciones). Este documento presenta el Estándar Prop Trust Verified (PTVS), una metodología forense integral diseñada para resolver esta brecha. Desarrollado por Aurema Group, PTVS establece un protocolo riguroso de auditoría física de activos, combinando la pericia judicial certificada con el anclaje criptográfico. La metodología garantiza que las inspecciones físicas, verificaciones de materiales y controles de cumplimiento se registren de forma inmutable y se vinculen a contratos inteligentes (ej. ERC-3643), proporcionando confianza de grado institucional para Family Offices, plataformas de tokenización y organismos reguladores bajo marcos como eIDAS (UE 910/2014). Este informe detalla los principios fundamentales, flujos de trabajo de verificación y aplicaciones prácticas de PTVS en los sectores inmobiliario y naval.
Alternative finance platforms, including crowdfunding, peer-to-peer lending, equity-based platforms, and token-based fundraising mechanisms, have become important channels for financing entrepreneurial, social, and investment-oriented initiatives. Yet their reliance on digital intermediation, dispersed participation, and information asymmetry creates opportunities for fraud, undermining trust, investor protection, and platform sustainability. This study provides a systematic review of fraud detection and prevention in alternative finance, with crowdfunding emerging as the most extensively represented empirical domain. Methodologically, the paper combines a PRISMA-guided systematic literature review with a hybrid topic-modeling strategy that integrates neural topic modeling and probabilistic refinement, thereby supporting both transparent corpus selection and data-driven thematic synthesis. The findings show that Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and blockchain-based mechanisms are recurrently discussed as promising tools for detecting, preventing, or mitigating fraud. AI and ML approaches are mainly used to identify anomalies, suspicious textual patterns, behavioral signals, and transaction irregularities, while blockchain-based approaches are associated with transparency, traceability, smart contracts, and conditional fund release. The review also shows that fraud differs across alternative finance models, ranging from campaign misrepresentation and intentional and premeditated non-delivery in crowdfunding to borrower or platform misreporting in lending-based models and misleading disclosures or white-paper manipulation in ICO/STO contexts. A central challenge across the literature is the scarcity of labeled fraud data, which limits the use and benchmarking of supervised ML models. Overall, this study contributes by linking a reproducible hybrid SLR methodology to a structured synthesis of fraud types, platform-specific vulnerabilities, and AI-, ML-, and blockchain-based detection strategies in alternative finance.
A Decentralized Application (DApp) is a distributed, open-source software application that runs on a peer-to-peer (P2P) blockchain network. DApps are emerging as a transformative force across various sectors, leveraging blockchain technology to create applications that operate autonomously, offer enhanced security and transparency, and function without a central authority. Common security issues with DApps include smart contract and blockchain vulnerabilities, phishing and social engineering attacks, and key management challenges. Mitigation strategies involve thorough code audits, formal verification, multi-signature wallets, and robust security frameworks. This chapter provides a detailed analysis of DApp security, focusing on theoretical aspects, architectural components, and specific vulnerabilities in smart contracts, oracles, blockchain protocols, front-end interfaces, and cross-chain interoperability mechanisms.
Validation of a secure blockchain architecture's effectiveness and robustness may be achieved via a methodical process that involves comprehensive testing and implementation in real-world environments. The testing process includes analyzing requirements, setting up the environment, and conducting detailed evaluations. All aspects of the smart contract, from its logic and functionality to its defences against common attack vectors like Sybil and re-entrancy vulnerabilities, are evaluated in these reports. Performance testing under different loads and realistic network conditions is essential for assessing scalability, latency, and throughput, in addition to API and integration testing, which ensure that system components operate well together. System resilience to defects and hostile events is tracked, critical test cases are automated, and large-scale peer-to-peer networks are modelled to evaluate the framework's robustness further. Supply chain verification and clinical trial administration are two examples of real-world applications of blockchain technology that demonstrate its ability to secure sensitive activities on a large scale. These use cases also give light on the system's efficacy, data integrity, and anomaly detection capabilities. Verifying the scalability, security, and reliability of a blockchain architecture against real business objectives via integrated deployments and complicated testing methods is essential for a safe blockchain.
A blockchain-enabled AI healthcare system is proposed to enhance disease prediction and secure healthcare data management. The framework integrates advanced artificial intelligence techniques with robust data security mechanisms to ensure accurate forecasting and safe handling of sensitive medical information. The system employs a hybrid GRU–LSTM model for real-time chronic disease prediction using electronic health records, IoT sensor outputs, and wearable device data. To further improve prediction performance, the Fireworks Algorithm is utilized for hyperparameter optimization and feature selection. Patient data are encrypted using RSA-2048. The model is evaluated using the ‘Disease Prediction Using Machine Learning’ dataset, which contains demographic and clinical attributes for predicting diseases such as diabetes. Experimental results demonstrate strong predictive performance with 99.87% accuracy, 98.46% precision, 98% recall, and a 97.53% F1-score. In addition, the blockchain layer provides high operational reliability, achieving 99.99% data integrity and system availability, 100% auditability, and 99.8% data-sharing efficiency. The platform supports approximately 1,500 transactions per minute and implements role-based access control through smart contracts with an execution time of 0.2 s. Overall, the integrated AI–blockchain framework offers a scalable, transparent, and privacy-preserving solution suitable for real-world healthcare applications, including hospital decision-support systems and remote patient monitoring.
The exponential emergence of cross-chain data sharing in blockchain-enabled IoT and cloud systems creates vital challenges in the scalability, privacy, and post-quantum security landscapes. To tackle these problems, we propose a hierarchical attribute clustering-attribute-based encryption (HAC-ABE) scheme in this paper, which offers a secure post-quantum cross-blockchain data exchange framework. The method utilizes hierarchical attribute clustering and lattice-based encryption to reduce the computation overhead while supporting fine-grained access control. It utilizes IPFS decentralized storage and smart contracts to achieve a transparent data exchange across chains. Experimental results show 9.7% faster computation time and much lower communication overheads than state-of-the-art ABE-based approaches, verifying its effectiveness and scalability for practical decentralized environments.
Newborn misidentification poses serious patient safety and accountability problems, but errors can be traced through the use of a blockchain to create an audit trail. However, a blockchain storing raw or even hashed biometric templates for individual identities is not acceptable for privacy reasons. This work redefines our prior work (1) to form a privacy-preserving audit protocol that isolates the processes of capturing a biometric and matching it against a database of known identities to an external Service Provider and the processing of the blockchain to a permissioned Ledger that contains only pseudonymous audit commitments related to keyed entries on the Ledger. This work describes an implementation of this protocol in Solidity 0.8.30 and provides metrics for the gas use and latency of the smart contract for 100 iterations of 100 total Enrollment and Verification Workflows each. Twenty Adversarial Functional Tests are also described that attempt to place the system into an invalid state, as well as four additional tests that assess the effect of batched submission to the smart contract of multiple keyed audit commitments. The smart contract processing throughput is also determined for a batch of submissions, finding a maximum local throughput of 60.2 tx/s. A further 50,000 randomized reference-model transitions of the system’s internal reference-model were then made (involving a total of 57,345,087 invariant checks, all of which passed), as well as a measurement of the time taken to generate an HMAC-SHA-256-sized commitment for 10,000 iterations (local median time = 0.002 ms). The results of this work provide a solid foundation for the blockchain component of BIBIS, but it is not intended to provide any insights into the accuracy of neonatal biometric matching, the presentation attack resistance of the system, or even the usability of BIBIS by clinical end-users. The results also do not comment on the finality of QBFT-based commits to a blockchain.
As the digital landscape expands, centralised cybersecurity frameworks grow increasingly vulnerable to sophisticated threats, creating single points of failure and targets for adversarial data manipulation. While AI enables real-time threat detection and big data analytics, its centralised deployment limits efficacy and exposes training data to poisoning and evasion attacks. To address this, the AICyber-Chain model proposes a distributed framework combining parallel AI and blockchain architectures. It leverages a hybrid Proof-of-Stake (PoS) and Byzantine Fault Tolerance (BFT) mechanism with IPFS and Private Data Centres (PDCs) for secure decentralised storage and processing. Generative Adversarial Networks (GANs) refine security rules, while Ethereum-based smart contracts enable automated responses and trustless data sharing. Results on the Rinkeby test network show 1.8× faster authentication, 25% lower gas consumption, F1 score of 0.92, and 1.2 s response time, with a medical data sharing use case ensuring data provenance and tamper-proof control.
Cryptocurrency users have increasingly become targets of phishing and scam attacks. To mitigate these threats, leading crypto wallets (e.g., MetaMask) have introduced transaction simulation, which previews a transaction's balance changes before on-chain execution. While effective against traditional fund-draining attacks, we show that this defense can itself be exploited by a new phishing technique, which we term transaction simulation phishing. This attack uses carefully crafted smart contracts whose execution depends on dynamic blockchain state, causing simulations to display benign or profitable outcomes while the actual on-chain execution redirects users' funds to attacker-controlled addresses. We present the first comprehensive study of transaction simulation phishing. We first develop a taxonomy of phishing contracts that can be utilized to facilitate this attack. Then, we propose SIMGUARD, a bytecode-level detection system that combines static and dynamic program analysis to identify phishing contracts. Applying SIMGUARD to Ethereum, Binance Smart Chain, Avalanche, and Polygon, we detect over 4,000 phishing contracts deployed between August 2024 and June 2025. Our analysis identifies more than 5,700 victims and approximately $3.48 million USD in losses, 91.5% of which occurred on Ethereum. Moreover, our clustering result reveals that the largest phishing contract cluster alone accounts for about 83% of the total losses. These results expose a critical weakness in current wallet defenses and highlight the urgent need for more robust transaction simulation mechanisms.
Xiaoye Zheng, Yujing Chen, Minghao Wu, David Lo · 8 authors
Solana is an emerging blockchain platform designed for high throughput and low transaction fees, making it inexpensive to submit transactions at scale and, consequently, increasing exposure to bot spamming and related financial exploitation. Solana bots are typically off-chain software systems that operate in a competitive on-chain execution environment by constructing and submitting transactions, and the bot-related transactions on the decentralized exchanges exceed 250 million dollars in daily trading volume in January 2026. Prior studies on Solana have examined system performance, smart-contract security, and specific on-chain phenomena. However, we still lack a systematic understanding of what Solana bots implement in practice and how these implementations manifest as observable on-chain execution fingerprints. To address this gap, we performed a large-scale empirical study of Solana bots from two complementary views: (i) 586 bot repositories collected from GitHub, and (ii) 200 bot addresses on Solana, with over 44 million on-chain transactions. Our study derives an implementation-grounded taxonomy of Solana bots comprising 15 categories grouped into five domains (e.g., Trading Operations, MEV, and On-chain Analytics), identifies a largely shared five-stage operational pipeline manifested in bot implementations, and uncovers systematic variation in on-chain trading behaviors of Solana bots across diverse trading platforms and assets. Based on our findings, we highlight future research directions, and provide recommendations for building and operating bots on the Solana blockchain.
This article presents the DigInTraCE Blockchain Module, a secure and scalable framework for managing Digital Product Passports (DPPs) and traceability data across industrial supply chains. Built on Hyperledger Fabric, the solution combines distributed ledger technology, cloud-native infrastructure, smart contracts, and standardized EPCIS 2.0 traceability to enable trusted collaboration among multiple stakeholders. The technical article describes the platform architecture, governance mechanisms, secure API integration, identity management, and blockchain-based validation processes that support transparent, interoperable, and auditable product lifecycle information. The proposed framework provides a robust foundation for future Digital Product Passport implementations and circular industrial value chains.
To managing identities in a secure and decentralized manner, new opportunities have emerged because of recent breakthroughs in blockchain technology and biometric authentication. Blockchain is different from traditional biometric systems in that it is an unchangeable, distributed ledger that runs safe, decentralized code. Traditional biometric systems store data in one location and can’t be updated. Traditional biometric systems have some flaws, including template tampering, channel interception, and comparator overrides. So, the proposed work presents a Distributed Multimodal Biometric Security System with Blockchain to handle such issues. This system uses 3D face and 3D ear biometrics with blockchain technology, which comprises IPFS, smart contracts, and decentralized applications. Features from 3D face and 3D ear are embedded into a single multimodal template, which then undergoes encryption and storage on IPFS via content-addressed storage. The Content Identifier (CID) and data are then archived by smart contracts on the blockchain to maintain data integrity, security, verifiability, and immutability. In this way, a person can prove his identity without using any central services, further improving privacy. Blockchain consensus and the smart-contract-based access control mechanism further provide security, audibility, and simplicity to P2P transactions in biometric enrolment testing results show that feature extraction takes from 120 ms to 300 ms, uploading to IPFS takes between 200 and 600 ms, and completing blockchain transactions on local private network takes from 0.5 to 1 s, using 117,519 gas per enrolment. Additional analysis on the Ethereum Sepolia test network reveals that transaction fees change depending on network conditions, but gas consumption stays deterministic. The suggested solution is resistant to typical attacks like replay, interception, and template alteration; it is also irreversible, revocable, and unlinkable, according to security analysis conducted under a formal adversarial model.
Blockchain is becoming an approachable data platform with several stakeholders having a shared history of transactions without being owned by an individual. The fundamental concepts of cryptographic hashing, peer-to-peer communication, and agreement protocols are sufficiently documented, and a lot of current research focuses on performance, security, privacy, and governance individually rather than as interacting dimensions. Recent blockchain research publications and deployments were structured based on a four-axis perspective that follows the dynamics of a system in terms of security, scalability, privacy, and governance. This lens was applied to consent mechanisms including proof-of-work, proof-of-stake, and practical Byzantine fault tolerance, and to techniques such as sharding and layer-2 that are designed to enhance throughput. Basic throughput calculations using reported block sizes, transaction sizes, and block intervals indicate that configuration limits are usually much larger than the transaction rates achieved in practice, implying that protocol overheads and network behaviour require a major share of the budget. A survey of attacks and defences indicates that increases in speed and programmability often expand the attack surface at the consensus and smart contract layers, which motivates the development of better analysis and monitoring tools. The results are applied to draw design insights for domains of finance, supply chains, healthcare, identity, and smart city platforms, and to highlight remaining problems in benchmarking and cross-chain coordination. A practical mapping of major blockchain platforms, namely Bitcoin, Ethereum, and Hyperledger Fabric, onto the four-axis framework was also provided to demonstrate its utility for platform comparison and selection.
Counterfeit medicines, fragmented record management, limited end-to-end visibility, and dependence on centralized databases create security and traceability challenges in pharmaceutical supply chains. This article presents Pharma-Chain, a blockchain-enabled pharmaceutical supply chain management system developed to record medicine registration, stakeholder interactions, ownership transfer, and authenticity verification on an Ethereum blockchain. The system integrates a React.js frontend with Web3.js, MetaMask wallet authentication, Solidity smart contracts, Truffle deployment tools, and Ganache for local blockchain development and validation. Manufacturers register medicine batches with product and lifecycle information; distributors and retailers verify blockchain records before accepting and transferring ownership; and customers retrieve medicine details and transaction history before purchase. The implementation was tested module by module and as an integrated application in a local Ethereum environment. Testing confirmed successful authentication and role-based access, medicine registration, ownership transfer, transaction validation, blockchain record retrieval, and medicine verification. Each successful supply-chain operation generated a blockchain transaction record, providing an immutable ownership history and improving transparency and auditability. The project demonstrates a practical decentralized approach for secure pharmaceutical tracking while identifying QR-code verification, IoT monitoring, AI-assisted analytics, mobile access, and deployment on public or enterprise blockchain networks as future extensions.
Open access
2 source records
Blockchain Technology Applications and Security
Pharmaceutical Quality and Counterfeiting
Physical Unclonable Functions (PUFs) and Hardware Security
Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasingly use deep learning and treat blockchain activity as a quasi-linguistic sequence. However, complex sequence models are computationally expensive for real-time monitoring, and their reported performance may be inflated by label leakage from high-signal smart contracts. We ask whether and how organic users, Sybil bots, and MEV bots differ in the structural complexity of their transaction histories; whether sequential models outperform tree-based tabular models once leakage is reduced; whether transaction order or timing provides the stronger behavioral signal; and whether the resulting models are practical for low-latency deployment. Our approach to leakage-aware Sybil bot detection consists of a Blind-Spot protocol and a Transaction Grammar representation of wallet behavior. The former eliminates shortcuts associated with high-signal contracts, whereas the latter models wallets using rhythm, EVM execution structure, and intent. We evaluate this approach on Ethereum actor classification by comparing Transformer and BiLSTM sequence models against XGBoost and SVM baselines. We contribute a framework for leakage-aware Ethereum actor classification and a Transaction Grammar representation of wallet behavior. Our results demonstrate that, under leakage-aware evaluation, XGBoost outperforms Transformer-based sequence models while providing lower latency and estimated energy use.
This article is devoted to the general legal characteristics of smart contracts as a means of disposing of digital assets. Smart contracts are a key mechanism for transferring digital assets, but their legal regime remains controversial and unexplored. The main goal. To make a general civil law qualification of smart contracts as a special form of concluding transactions by order of the Central Bank and to justify their recognition as full-fledged legal means of achieving a legal result. The problems under consideration are the differentiation of the IC from related legal phenomena: computer programs, methods of securing, executing and concluding a transaction. The methods used are: formal-legal, comparativelegal, historical-legal, systematic. Conclusions. The IC is a special digital form of the transaction, which has the highest degree of reliability and accuracy of recording the will of the parties in the presence of objective guarantees of its execution.
Smart contract vulnerability detection requires evaluation protocols that separate real representation signal from dataset-specific artifacts. DIVE provides lifecycle-based tabular features for Ethereum smart contracts, but benchmark performance alone cannot show whether a dominant feature group is useful or only benefits from having many columns. This study examines Opcode Distribution features using 22,330 contracts, 397 processed features, and eight DASP-aligned vulnerability labels. Five multi-label learning configurations were evaluated under 3 x 5 repeated cross-validation, followed by global feature-group ablation, size-controlled random opcode ablation, per-label degradation analysis, cumulative stability analysis, and opcode-profile group-aware robustness checking. MultiOutput LightGBM achieved the best baseline performance, with Micro-F1 of 0.91396, Macro-F1 of 0.82464, and Macro-PR-AUC of 0.90146. Removing the full Opcode Distribution group reduced Macro-F1 to 0.78745, while removing a same-sized random opcode subset produced Macro-F1 of 0.82404. The findings indicate that Opcode Distribution acts as a collective predictive representation rather than a feature-count artifact, without implying causal vulnerability mechanisms.
With the advent of digital services, various vulnerabilities in centralized identity management systems such as Single Point of Failure (SPOF), data leakage, and user privacy invasion are evident. In this paper, an architecture framework for designing Decentralized Identity Management System (DIDMS) by leveraging blockchain technologies is proposed for privacy preserving authentication in digital age. The DIDMS framework makes use of Decentralized Identifier (DID) with Verifiable Credentials (VC) and Zero Knowledge Proof (ZKP) techniques to implement the concept of self-sovereign identity. Four phases of framework including registration, credential issuance, authentication and storage is designed and tested. The performance results obtained from the experimentation indicate that the proposed framework gives an authentication latency of 320 ms, storage overhead reduction of 42%, and verification accuracy of 98.6% with 31% gas optimization over conventional smart contract approaches. The comparative evaluation with existing frameworks indicates improvement in the areas of privacy protection, scalability and user control.
Prajakta Khule, K. Kumaraswamy, Puja Bhardwaj, Meghana Bhilare
Abstract The increasing complexity of global financial systems has exposed the limitations of conventional centralized banking infrastructures in managing transparency, operational efficiency, security, and real-time transaction processing. Distributed Ledger Technology (DLT), particularly blockchain, has emerged as a transformative digital architecture capable of addressing these structural challenges through decentralized record management, cryptographic security, and automated transaction validation. This study examines the architectural foundations and strategic viability of blockchain-enabled distributed ledger technologies within modern banking and corporate finance. Using a qualitative research approach based on an extensive review of recent scholarly literature, industry reports, and practical financial applications, the study evaluates how different blockchain architectures contribute to organizational transformation. Three representative case studies—consortium corporate lending and syndicate management, cross-border settlement systems, and decentralized Know Your Customer (KYC) identity management—are analyzed to demonstrate the practical implications of enterprise blockchain adoption. The findings indicate that permissioned and consortium blockchain architectures significantly enhance operational transparency, reduce intermediary dependence, improve data integrity, automate compliance through smart contracts, and accelerate financial transactions while strengthening governance and auditability. However, the study also identifies challenges associated with regulatory uncertainty, interoperability with legacy systems, scalability, and institutional readiness that continue to influence large-scale implementation. The research contributes to the growing body of knowledge by integrating architectural analysis with strategic business evaluation and proposes a comprehensive perspective on the role of distributed ledger technologies in reshaping banking operations and corporate financial management. The findings provide useful insights for researchers, financial institutions, technology professionals, and policymakers seeking to develop secure, efficient, and sustainable digital financial ecosystems. Keywords: Distributed Ledger Technology (DLT), Blockchain, Smart Contracts, Consortium Lending, Cryptographic Auditing, Cross-Border Clearance, Financial Disintermediation, Asymmetric Cryptography, Banking and Finance.
Relating low-level executable code to a high-level account of its behavior has been a central concern of programming-language research for decades. From formally verified compilers to translation validators, certifying compilers, and proof-carrying code, each approach chooses between laborious but foundational mechanized proofs and automation that costs completeness, generality, and an increased trusted base. Recently, large language models (LLMs) have begun to change the economics of formal verification. Agentic proof development is now capable of producing machine-checked proofs at a scale and speed that were previously out of reach. In this paper, we evaluate the capabilities of LLMs to produce foundational, machine-checked proofs of refinement between executable code and its high-level specification, as post hoc, per-artifact certificates. We study this in the context of the Ethereum Virtual Machine (EVM), a low-level virtual machine that executes smart contracts on the Ethereum blockchain. We build EquiVM, a foundational framework in Lean comprising an executable EVM semantics and a specification language that characterizes the intended behavior of smart contracts, but commits to no source language or compilation toolchain. In EquiVM, refinement is stated for deployed bytecode of arbitrary provenance, interaction with unknown code is part of the semantics, and each proof is a replayable, machine-checked certificate. No previous technique achieves this combination. Using frontier commercial LLMs, twenty-three real-world contracts are proved end to end with minimal human guidance, among them most of the MakerDAO stablecoin system, at up to a hundred million tokens and a hundred hours of proof time per contract. We conclude that foundational mechanized proofs can now be bought at the price of tokens, and that this shift can reshape how verification frameworks are architected.