Blockchain Papers

Muhammad Umar Janjua, Akshaya Mani, Uğur Şen, Daniel Kaiser

Privacy and anonymity of validators, especially regarding IP address linkability, are essential to protect the Ethereum network from various attacks. Network-level attacks, such as DoS, can interrupt validators and affect the overall security of the Ethereum network. Correlating the IP addresses of validators with their identities, along with knowledge about their action slots can be exploited by attackers to cause network delays, MEV exploitation, and finality risks. Therefore, ensuring the unlinkability of a validator's IP and identity is crucial for maintaining the network's trust and resilience. In this techreport, we first provide a review of the existing network and consensus layer techniques that have been proposed for maintaining validator privacy in the Ethereum blockchain. Secondly, we evaluate a Tor-based protocol named Tor push that helps unlink validator identities (IDs) from their nodes' IP addresses, thereby making it difficult to determine any end-to-end correlation between validator IDs and IP addresses of validators' beacon nodes. To evaluate the effectiveness of Tor push, we present a working, deployed proof-of-concept (PoC) implementation in the Nimbus Ethereum client. Our PoC deployment pushes attestations, aggregations, and block proposals over Tor to the Goerli testnet. Furthermore, we also analyse the security and latency of Tor push. Our experimental results suggest that Tor can be incorporated into the existing Ethereum network with a tolerable latency overhead of 613.82 ms on average and without compromising the overall network performance while enhancing the location privacy of validators in the Ethereum network.

Zishan Ashraf Mohammad, Nick Harkiolakis, Saman Sarbazvatan

Although there has been a massive increase in the size and complexity of the cryptocurrency ecosystem, most of the academic research into the relationship between token design parameters and the long-term value of a given token is still very much in its infancy. Most of the research in tokenomics is theoretical in nature, based upon frameworks for understanding, or is focused solely on observing a specific time frame. The authors of this paper address the above mentioned void by studying the statistically significant relationships between five on-chain tokenomic variables--transaction gas fees, total value locked (TVL), token unlocks, tokens burned, and governance concentration (as measured using the Gini coefficient) -- and the market price of Ether (ETH) during a 52 months observation window that began in August 2021 and ended in September 2025. The data for the study consisted of bi-weekly observations (n = 108) which allowed researchers to use three different analytical methods--Spearman correlation analysis, log-linear multiple regression analysis, and an error correction model (ECM) after conducting Johansen cointegration and unit root tests. A cointegrating equation among the variables was established through Johansen Trace Testing, indicating that all of these variables do indeed exhibit a long-run equilibrium relationship. The ECM revealed that the total amount of funds “locked” into smart contracts (“total value locked”) was the strongest single predictor of the price of Ether in both the long run (beta = 0.8, p < 0.001) and short run (beta = 1.18, p < 0.001) specifications. Additionally, it was found that token unlocks have a negative relationship with price (beta = −0.22, p < 0.001). Gas Fees (beta = 0.2, p = 0.021) and tokens burned (beta = 0.15, p = 0.039) had positive coefficients at the 0.01 level in the long-run specification; however, both exhibited extremely high levels of multicolinearity (Variance Inflation Factor>28,000), likely due to their technical/operational linkages under EIP-1559. Voting power did not demonstrate a statistically significant relationship to price (rho =0.143, p > 0.05).

Ide Prima

Pesatnya perkembangan teknologi finansial telah melahirkan aset digital baru berupa Cryptocurrency dan Non-Fungible Token (NFT) yang memiliki nilai ekonomi signifikan. Namun, regulasi di Indonesia saat ini lebih menitikberatkan pada aspek perdagangan (komoditas) melalui aturan Bappebti, sementara pengaturannya sebagai objek hukum dalam ranah keperdataan, khususnya hukum kewarisan, masih belum spesifik. Penelitian ini bertujuan untuk menganalisis kedudukan hukum aset digital sebagai objek waris menurut Kitab Undang-Undang Hukum Perdata (KUHPerdata) dan mekanisme pemindahannya kepada ahli waris. Metode penelitian yang digunakan adalah yuridis normatif dengan pendekatan perundang-undangan (statute approach) dan pendekatan konseptual (conceptual approach). Data yang digunakan adalah data sekunder yang terdiri dari bahan hukum primer, sekunder, dan tersier. Meskipun bersifat imateriel, Cryptocurrency dan NFT memenuhi kualifikasi sebagai "Benda" (Zaak) bergerak yang tidak berwujud sebagaimana diatur dalam Pasal 499 dan Pasal 503 KUHPerdata, karena memiliki nilai ekonomi dan dapat dimiliki secara hukum. Oleh karena itu, aset digital secara yuridis sah untuk dikategorikan sebagai bagian dari harta warisan (boedel waris). Dalam pewarisan aset digital terletak pada sifat anonimitas dan desentralisasi teknologi blockchain. Tanpa penyerahan private key atau akses dompet digital dari pewaris kepada ahli waris, aset tersebut terancam menjadi "aset beku" yang tidak dapat dieksekusi meskipun secara hukum hak kepemilikannya telah berpindah demi hukum (Le Mort Saisit Le Vif). Diperlukan pembaharuan hukum atau pedoman teknis mengenai tata cara pembuktian kepemilikan dan prosedur eksekusi aset digital dalam penetapan waris agar memberikan kepastian hukum dan perlindungan hak bagi ahli waris.

Amina Ladhari, Heni Boubaker

This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.

Mishall Hammed Al-Zubaidie, Wid Alaa Jebbar

Digital loans offer rapid, simple, and usually paperless transactions, and have radically changed the lending industry. The whole loan process is easy to access and effective; there are a number of threats associated with the availability of data online. As a crucial kind of digital loan, flash loans put additional pressure on banks to maintain security because they return to the same block of the Blockchain, making it more likely that they will be tampered with. It is important to address the possibility of predatory lending practices that target weaker payees. As a result, this chapter has created a security protocol based on the idea of digital wallets with self-sovereign identity (SSI) and decentralized finance (DeFi) for cryptocurrencies, which are secured by the Xsalsa20 algorithm. It has satisfied specs and is further improved by the application of the Crow search algorithm (CSA), which ensures quick and effective search results. We were able to verify several security features, including data authenticity and complete forward secrecy, by analyzing the suggested system.

Aniket Warule, Sana Shaikh, Kunal Darekar, Sudarshan Bankar · 5 authors

The emergence of Decentralized Autonomous Organizations (DAOs) represents a paradigm shift in organizational governance, yet their technical complexity remains a significant barrier to widespread adoption. Creating and managing a DAO requires deep expertise in blockchain development, smart contract auditing, and cryptocurrency operations, which excludes many potential users in non-technical domains. This paper presents DAOship, a novel no-code platform for DAO creation and management deployed on the Avalanche blockchain. The platform provides an intuitive graphical user interface (GUI) that allows users to configure, launch, and operate a fully-functional DAO without writing a single line of code. By leveraging Avalanche's high throughput and low transaction fees, the system enables the deployment of customizable smart contracts for governance, treasury management, and voting. The platform dramatically lowers the technical barrier, empowering communities, startups, and traditional organizations to leverage decentralized governance models easily and securely. Testing on the Avalanche Fuji testnet yielded a 97% reduction in setup time, an average System Usability Scale (SUS) score of 89.2, and zero critical vulnerabilities across all deployed DAOs.

Abishai Joy Paul, B u Muthamma

Blockchain technology and cryptocurrency have emerged as two of the most consequential financial innovations of the past two decades, yet the gap between their theoretical potential and real-world adoption within mainstream financial services remains conspicuously wide. This paper investigates that gap through a mixed-methods approach, combining a systematic review of thirty peer-reviewed academic sources with primary survey data drawn from 102 respondents representing young, digitally literate demographics. The study finds that while awareness of blockchain and cryptocurrency is relatively widespread, deep comprehension, active usage, and genuine user trust remain limited. Survey respondents show cautious optimism rather than firm conviction — the majority are open to engaging with blockchain-based financial services but are held back by concerns over security, regulatory legitimacy, and a general unfamiliarity with how these technologies actually function. The research identifies four interconnected barriers to adoption: trust deficits, regulatory fragmentation, scalability constraints, and the persistent gap between surface-level awareness and functional understanding. The study concludes that blockchain and cryptocurrency are not questions of 'if' but of 'when' and 'how' — and that realising their potential will require coordinated effort from regulators, financial institutions, technology developers, and educators acting simultaneously rather than sequentially.

Osvaldo Janeri Filho

Como a perícia blockchain organiza evidências para vítimas e advogados Golpes com criptomoedas costumam envolver promessas de investimento, falsas corretoras, pirâmides, phishing, malware, engenharia social e transferências para carteiras controladas por fraudadores. A boa notícia é que blockchains públicas deixam rastros verificáveis. A má notícia é que transformar esses rastros em prova útil exige método. O que a perícia blockchain consegue mapear Uma análise técnica pode identificar transações de entrada e saída, carteiras intermediárias, consolidação de valores, uso de bridges, mixers, exchanges, contratos de tokens e movimentações de stablecoins como USDT e USDC. O objetivo é reconstruir o caminho do ativo e apontar possíveis pontos de identificação.

Drissia Ennagoura, Kamal El Kehal, Safae Merzouk, BERDAI ABDELHAMID · 8 authors

Prices of cryptocurrencies are tough to forecast due to their high volatility and susceptibility to abrupt market changes. This paper compares four models—ARIMA, Prophet, LSTM, and XGBoost—to predict Ethereum (ETH) prices on three horizons: 15 minutes, 1 hour, and 1 day. We compared all four models concerning Root Mean Squared Error (RMSE) from the historical ETH data. The outcome shows XGBoost performs best on short-term forecasting with an RMSE of 352 in 15-minute and 357 in 1-hour data, surpassing LSTM and ARIMA. For the daily prediction, Prophet shows competitive performance with an RMSE of 941, whereas ARIMA is generally stable. The findings conclude that the ideal model depends on the forecasting horizon, and for short-term trading, using XGBoost is advisable, while Prophet is advisable for longterm forecasting. The study provides valuable recommendations to investors and researchers seeking effective cryptocurrency prediction software.

Tudor-Gabriel BUDISTEANU

This paper provides a literature-based analysis of the emerging role of blockchain technologies—specifically digital tokens and decentralised autonomous organisations (DAOs)—in shaping decentralised virtual economies. It focuses on the technological and economic infrastructure that enables immersive digital ecosystems often referred to as the metaverse. The study employs a critical review of academic literature, policy reports, and case studies published between 2016 and 2024 to examine howtokens (both fungible and non-fungible) facilitate economic exchange, digital ownership, and community-led governance. Through this methodological approach, the paper aims to clarify the transformative role of tokens and DAOs in enabling decentralised decision making, asset verification, and economic activity in blockchain-powered virtual environments. The main findings show that tokens serve dual functions: as a means of exchange (via cryptocurrencies and stablecoins), and as proof of ownership (through NFTs), while DAOs provide democratic governance frameworks for collective decision making. Platforms like Decentraland and The Sandbox illustrate these principles in action, enabling token-based voting, NFT-based asset trading, and decentralised funding allocation. However, several structural challenges persist. These include legal ambiguity, scalability limitations of current blockchain architectures, crypto market volatility, and unequal governance power distribution within DAOs. The paper also identifies emerging solutions, such as the use of stablecoins, integration of Central Bank Digital Currencies (CBDCs), and adoption of energy-efficient consensus protocols. In conclusion, the review argues that tokens and DAOs are not only technical tools, but foundational components of an evolving decentralised digital economy. Their adoption may redefine economic participation, governance, and asset ownership in virtual environments.

Chris Muellerleile, Matt Zook

No abstract is available for this record.

Irada Baghirova

Abstract. Transaction costs are considered one of the key factors determining the efficiency of market mechanisms in modern economic systems. Contract enforcement, data collection and verification, trust assurance between parties, and monitoring mechanisms generate additional costs for economic agents. In particular, in the context of global trade and the digital economy, the increase in these costs can limit the efficiency of market operations. In recent years, the rapid development of blockchain technology has created new institutional and technological opportunities to reduce transaction costs. This distributed ledger technology minimizes the need for intermediaries, ensures data immutability, and enhances transparency in economic relations. This paper analyzes the role of blockchain technology in reducing transaction costs based on economic theory and existing scholarly approaches. Within the research framework, the mechanisms through which blockchain technology addresses information asymmetry, automates contract enforcement, and strengthens trust mechanisms are examined. The main objective of this study is to evaluate the potential efficiency benefits of blockchain technology and scientifically demonstrate its strategic importance in reducing transaction costs.

Pim Keer, Ioannis Alexopoulos, Matteo Maffei, Marco Argentieri · 6 authors

Bitcoin is the cryptocurrency with the largest market capitalisation, but its widespread adoption is fundamentally limited by the scalability constraints of its consensus algorithm, which requires every transaction to be confirmed onchain. To address this, several Layer-2 scalability solutions have been proposed to move payments offchain -- most notably, the Lightning Network. However, their deployment remains hindered by cumbersome setup requirements: users must lock funds onchain to participate and engage in complex auxiliary protocols (e.g., for channel rebalancing, top-ups, and routing). Other solutions, like payment pools, sidechains and rollups, cannot be implemented in a non-custodial way on Bitcoin due to its limited scripting capabilities, or require all protocol participants to update the offchain state. In this work, we present Ark, the first Bitcoin-compatible commit-chain. Ark enables offchain transactions of virtual UTXOs (VTXOs), through an untrusted operator who aggregates them into succinct onchain commitments. A distinctive feature of Ark is its ease of deployment: users can receive offchain payments without locking any funds beforehand and Ark state updates can be performed only requiring the users involved in that update. We formally define the Ark protocol and prove its security. During this process, we identified two attacks affecting the testnet implementation, which we responsibly disclosed and proposed fixes for, which have been now integrated into the mainnet implementation. Our experimental evaluation demonstrates that Ark can commit onchain to batches of arbitrarily many VTXOs with a constant-sized footprint of approximately 200 vB. Cooperative exits add one output per user, while unilateral exits require $\mathcal{O}(\log n)$ transactions of roughly 150 vB per VTXO for a batch of $n$ VTXOs.

Andrei Bysik, Robert Ślepaczuk

This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs. Using approximately 70,000 hourly observations from 2018-2026, XGBoost, LSTM, and iTransformer are evaluated in a 27-fold walk-forward protocol. All three models produce positive gross trading performance in selected configurations, but naive sign-based strategies fail once transaction costs of ten basis points are imposed. A cost-aware execution filter, which prevents trades only when the forecast magnitude exceeds a transaction-cost-based threshold, sharply reduces turnover and restores profitability in selected configurations. The strongest long-only XGBoost strategy produces annualised returns above 65% with a Sharpe ratio above one. Additional tests show that technical indicators improve performance in selected cases, EGARCH-derived features do not provide uniformly robust gains, and XGBoost is descriptively stronger than the neural alternatives, although bootstrap evidence does not support formal statistical dominance. Loss-function and model-selection effects are secondary and statistically fragile. The results show that the main obstacle in hourly cryptocurrency trading is not only weak predictability, but also the way forecasts are converted into trades.

David Huk, Dongshan Wang, Miha Bresar

Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge during extreme market events. While modern diffusion-based models have advanced multivariate forecasting, they often suffer from a "normality bias" when trained end-to-end, sacrificing marginal calibration for joint coherence and consistently underestimating tail risk. To address this, we propose a Diffusion-Copula framework that explicitly decouples the learning of marginal distributions from their dependence structure. We employ deep Mixture Density Networks to capture heavy-tailed asset dynamics, followed by a Classification-Diffusion Copula to model the joint dependence. Applied to cryptocurrency markets, our approach demonstrates superior performance over state-of-the-art baselines in forecasting systemic extremes of both marginal and joint events. Crucially, we demonstrate that while baseline models classify simultaneous market crashes as statistically impossible "Black Swans" (high surprise), our framework identifies them as "Expected Crashes" (low surprise), successfully preserving the correlation structure necessary for robust risk management during contagion events.

Muharem Kianieff

Stablecoins have been heralded as the future of money on distributed ledgers. As was discussed in the previous chapter, stablecoins purport to mitigate the wild fluctuations that are experienced by cryptocurrencies such as Bitcoin by providing for a one-to-one reserve of a denominated fiat currency that holders can redeem at any time. Yet, despite these built-in mitigating factors, stablecoins have still been plagued by runs and a lack of transparency into their operations. As such, the Central Bank Digital Currency (CBDC) provides an interesting opportunity to see if the digital equivalent of fiat currency can offer increased efficiencies over conventional paper-based currency. 1 Moreover, can these efficiencies be leveraged to other sectors of the economy thereby stimulating more economic growth for all?

Sarfaraz Ali Shah Syed

The past decade has witnessed unprecedented innovation in financial technology, most notably the rise of cryptocurrency and digital assets. This paper examines how these developments have fundamentally reshaped one of monetary economics’ most enduring concepts: the money multiplier. From Bitcoin’s emergence to today’s complex ecosystem of stablecoins and decentralized finance (DeFi), digital assets have created parallel monetary systems that challenge central banks’ ability to measure and control the money supply (Bianchi et al., 2021).This paper has three primary objectives. First, to develop a theoretical framework that extends Divisia monetary aggregation - the gold standard for measuring money’s liquidity services (Barnett, 1980) to include cryptocurrencies and related digital assets, building on recent work applying Divisia indices to crypto-inclusive money demand (Mumtaz et al., 2025). Second, to derive a new crypto-adjusted money multiplier that captures liquidity creation across both traditional and digital financial systems, integrating the concept of the "crypto multiplier" introduced by Garratt and van Oordt (2023). Third, to analyse the implications for monetary policy transmission and financial stability using a Dynamic Stochastic General Equilibrium (DSGE) model (Fernández-Villaverde et al., 2020), considering the growing synchronization between crypto and global equity cycles (Fund, 2023). By achieving these objectives, we provide policymakers, financial institutions, and researchers with tools to understand and navigate the hybrid financial landscape of the 2020s.

Lei Zhao, Abhay Bhaskar, Edgar Dobriban

AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) risk: an agent may execute harmful instructions embedded in untrusted inputs such as email, downloaded files, webpages, repositories, or group-chat messages. Existing evaluations are often small, purely simulated, or focused on a narrow set of channels. We introduce LivePI (Live Prompt Injection), a structured benchmark for IPI risk in a production-like but test-controlled environment. LivePI covers seven input surfaces, twelve attack/rendering families, and five malicious goals, including protected-information exfiltration, unauthorized security-control changes, unsafe code retrieval or execution, inbox-summary exfiltration, and cryptocurrency transfer. We run LivePI on a real virtual machine with live but test-controlled email, chat, web, local-file, repository, and wallet interfaces. Across GPT-5.3-Codex, Claude Opus 4.6, Gemini 3.1 Pro, Kimi K2.5, and GLM-5, total attack success rates range from 10.7% to 29.6%. Group-chat injection is uniformly successful across the evaluated backbones in our deployment, and repository-link attacks produce high-severity failures despite a small denominator. We also evaluate a two-layer defense consisting of prompt-level filtering and pre-execution tool-call authorization. In the GPT-5.3-Codex setting, the defense intercepts all tested malicious-goal completions in LivePI before execution while preserving benign utility on PinchBench-derived workloads.

Avni Gupta

Cryptocurrency has emerged as a transformative asset class, reshaping traditional investment and portfolio management strategies. This study explores the impact of cryptocurrencies on modern investment portfolios, highlighting their potential for diversification, risk management, and return optimization. The decentralized nature of digital assets, combined with blockchain technology, has introduced a new paradigm in financial markets. However, the high volatility of cryptocurrencies remains a significant challenge, affecting portfolio stability and investor confidence (Brière, Oosterlinck, & Szafarz, 2015). This research examines key factors influencing cryptocurrency investments, including market trends, risk exposure, regulatory developments, and institutional adoption. By utilizing statistical analysis and market data, the study evaluates the correlation between cryptocurrencies and traditional asset classes such as stocks, bonds, and commodities. The findings indicate that while cryptocurrencies can enhance portfolio diversification, they also exhibit greater price volatility than conventional financial assets (Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018). Additionally, the study investigates how institutional investors are integrating digital assets into their portfolios and examines the impact of regulatory policies on market stability. The results suggest that regulatory clarity significantly influences investor confidence and risk mitigation strategies (Auer & Claessens, 2020). Furthermore, Bitcoin’s role as an inflation hedge is analyzed, with evidence supporting its potential as a store of value during periods of economic uncertainty (Yermack, 2015). The study concludes that cryptocurrencies continue to represent an emerging yet highly uncertain asset class within modern portfolio management. While investors acknowledge the potential benefits of cryptocurrencies, including high return opportunities and portfolio diversification, significant concerns remain regarding market volatility, regulatory uncertainty, and long-term sustainability. The findings reveal that investors perceive cryptocurrencies as high-risk investments and remain cautious about their consistent performance compared to traditional financial assets. The study further highlights that uncertainty surrounding global cryptocurrency regulations and market stability limits broader investor confidence and adoption. Although digital assets possess the potential to transform investment strategies through technological innovation and decentralized finance, investors continue to adopt a balanced and risk-conscious approach toward cryptocurrency investments. Therefore, effective regulatory frameworks, investor education, strategic asset allocation, and continuous monitoring of market developments are essential for the sustainable integration of cryptocurrencies into modern investment portfolios.

Christos Michalopoulos

No abstract is available for this record.

Andreas Polyvios Delladetsimas, Elias Iosif, Stamatis Papangelou, George Giaglis

This article examines blockchain as an enabling technological component for data management tasks that are independent of currency-related functionality, a less-discussed aspect of a technology commonly associated with cryptocurrencies and decentralized finance (DeFi). Drawing on empirical findings from the DIGI4ECO project as a case study, we present a structured literature review and cross-domain analysis of blockchain-based data management systems (BDMSs), examine a representative permissioned BDMS implementation, and synthesize practical design guidelines and implementation insights for BDMS development. This perspective is motivated by core blockchain properties such as immutability and transparency, as well as by the observation that existing resources for BDMS development, including methods, tools, and best practices, remain fragmented and less developed than those available for more mature technologies.

Daniel González Cortés, Monomita Nandy, Suman Lodh

Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.

Tarkan Yavas, Arslan Brömme

The rapid adoption of Web3 infrastructures has led to a growing number of security incidents affecting cryptocurrency exchanges, custody services and blockchain-based platforms. While existing research predominantly focuses on vulnerabilities in smart contracts and blockchain protocols, a substantial portion of real-world losses originates from off-chain systems, organizational processes and human-centered operational workflows. This paper presents a qualitative, incident-based analysis of publicly documented, high-impact security breaches in the Web3 ecosystem, including the Bybit exchange incident (2025), the Ronin Network bridge compromise (2022), and the DMM Bitcoin exchange breach (2024). The selected cases are systematically analysed and mapped to established Web2 security reference frameworks, including OWASP-based vulnerability categories and organizational security control domains. The results indicate that dominant failure patterns in Web3 environments are insufficiently addressed by generic security control catalogues, particularly with respect to cryptographic key management, transaction approval governance, signer and validator infrastructure, third-party tooling dependencies, and human-in-the-loop processes. Based on these findings, this paper argues for the adoption of established information security management systems (ISMS) in Web3 organizations and derives a structured set of blockchain-specific cybersecurity control categories to operationalize existing ISMS frameworks for blockchain-based systems. The proposed categories aim to bridge the gap between generic security governance frameworks and domain-specific risks inherent to Web3 infrastructures.

Olena Onishchenko

No abstract is available for this record.