The decentralized ecosystem is claimed to avoid security risks caused by centralization. Decentralized services, such as crypto wallets and decentralized applications (DApps), are purported to offer more reliable security and better protect user privacy. However, our research suggests a different reality: centralized components or scenarios are still prevalent within decentralized ecosystems, introducing security risks typically associated with centralization. This work systematically investigated the centralized security risks in crypto wallets and DApps. We found seven security risks and developed a series of methods to identify these risks. The detection results indicate that centralized security risks are widespread in the decentralized ecosystem. Among the 28 Ethereum-recommended crypto wallets, 96.4% have security risks. Of the 78 Web3 sites (frontends of DApps), 100% contain third-party scripts, and 44.9% expose the user's address to third parties. Furthermore, we developed a high-precision automated tool and inspected 110,506 on-chain smart contracts (backends of DApps), discovering that 83.5% contain at least one security risk. These risks affect 260 well-known tokens with a combined market capitalization exceeding${\$}$98 billion.
Tanmay Thapliyal, Aman Gupta, Rachit Agarwal, Sandeep K. Shukla
The emergence of Blockchain 2.0, along with the introduction of smart contracts (SCs), has facilitated the development of automated decentralized financial interactions on various platforms. These programmable contracts are utilized in applications including decentralized finance (DeFi), token issuance, and automated fund transfers. However, the pseudonymous nature of blockchain transactions, combined with automation capabilities and mixing services, has been exploited by malicious actors to launder illicit proceeds. Most of the techniques in the state-of-theart approaches detect addresses related to such illicit actors by relying on machine learning techniques that use only transactionbased features. In this work, we propose an algorithm to detect and identify addresses that are related to scamming activities, such as phishing. We focus on Ethereum, one of the widely adopted blockchains, and analyze over 1.8 billion transactions to identify SC deployments, thereby creating an SC deployment mapping. Alongside this mapping, we construct a transaction graph of known scam-related accounts, which we use to identify accounts involved in laundering proceeds from these scams. This approach identifies $\mathbf{3 2, 2 7 2}$ accounts associated with known scam addresses. The proposed algorithm takes an average of 0.024 seconds to determine whether an address is illicit. We also identify three recurring motifs characteristic of scam-related addresses, which can aid blockchain forensic frameworks in detecting such activities. By validating our findings with crypto-forensic tools, we uncover additional malicious addresses and provide actionable insights for law enforcement agencies.
Cryptocurrency money-laundering forensic analysis after Web3 incidents faces challenges such as fragmented evidence, expanding transaction paths, and cross-chain discontinuity. Existing Web3 AML methods largely rely on manual clues and heuristic or graph-search-based tracing, with outputs limited to lists of suspicious addresses and lacking path-level evidence and verifiable explanations. Directly applying general-purpose large language models to raw transaction flows also struggles to ensure evidence constraints and result verifiability. To address these limitations, this paper presents RISKTAGGER, an LLM-guided agent for forensic tracing of Web3 cryptocurrency money laundering. RISKTAGGER embeds the LLM as an evidence-constrained decision component within a controlled tracing loop. It extracts case clues from public incident materials, recursively expands a risk-labeled fund-flow graph over on-chain evidence, and generates evidence-organized reports for analyst review. We evaluate it on five real-world incidents spanning multiple years and covering heterogeneous attack patterns and laundering path structures. We further conduct cross-case generalization analysis, baseline comparison, component ablation, and LLM backend analysis. In the main Bybit case, the system achieves a 97.33% address recall and a 98.69% expert-reviewed sampled address precision. Across the other four incidents, it achieves 95.24-100.00% address recall and 91.27-100.00% expert-reviewed address precision. The cross-case results further show that the complexity of Web3 money laundering arises from heterogeneous mechanisms, including short-cycle fund fragmentation, long-range laundering paths, interwoven DeFi services, and deterministic denomination splitting. RISKTAGGER can recover case-related fund paths, identify high-priority risk accounts, and organize public evidence into verifiable forensic reports.
The vulnerabilities in this vigorous digital landscape are taking a more sophisticated shape as the nature and danger of cyber threats and the nature of cyber threats are taking new forms as a zero-day attack, polymorphic malware, insider threat and advanced social engineering methodologies and where traditional reactive security measures are no longer relevant. This issue of organizations detecting the threat, and that they need to mitigate the threat in real time is rather of a challenge in the light of the fact the behaviour of the adversaries is very much similar to that of legitimate user behaviour and as such will create ambiguity issues that will make an organization believe that it has hit a false positive or missed a threat. A proactive approach to cyber threat countering, the Adaptive AI-Driven Autonomous Threat Hunting (ADCH) Framework, is the focus of this paper as it will be able to monitor, analyse, and mitigate the development of the new threats far before it becomes reality. Behavioural profiling and reinforcement learning are employed at ADCH to differentiate between innocent and harmful actors on the fly even in the ambiguous or complex situation, and ethical precautions are taken so that the deepfake interaction modules are tightly regulated and privacy safeguarding. The framework brings together automated threat intelligence extraction where Indicators of compromise (IoCs) are extracted, classified and pooled across different sources and offer rapid and actionable information. Additionally, ADCH is compatible with Siem and SOAR, automates the incident response and mitigation process to minimise latency and dependency on people. Secure logging provides auditability resistant to tampering and transparent recording of events without de-anonymization of sensitive operational data, with blockchain used to enforce the use of permissioned ledger systems, smart contracts, and zero-knowledge proofs. The results of simulation testify that ADCH has been doing consistently well in terms of detection performance and accuracy, precision, recall and F1 scores, the results stand at $80-90$ and far better than the traditional threat hunting systems. Autonomous AI-controlled detection, ethical simulation, automated intelligence extraction, coordinated response, and blockchain-based logging can be combined to help in establishing a strong and intelligent paradigm of defense against the emergent and advanced cyberspace threats and ensure the safety of the digital infrastructures, transparency, accountability and ethical standards.
Brandon Dulisse, Nathan T. Connealy, Amanda Harrison, Matthew W. Logan
The exponential rise of cryptocurrency has outpaced both understanding and safeguards regarding its utility, rendering crypto markets susceptible to fraud on a massive scale. In this paper, we seek to understand drivers behind female cryptocurrency purchasing behavior, as well as whether gender influences risk of victimization. Based on the analysis of a survey of over 900 cryptocurrency purchasers (33% female), this study explores the relationship between gender and a variety of influences related to cryptocurrency purchasing behavior. Our analysis revealed a significant relationship between gender and cryptocurrency knowledge as well as victimization. These findings have several implications, most crucially that female crypto purchasers may be differentially influenced by subcultural factors that increase risk of victimization compared to their male counterparts.
Svitlana Popereshnyak, Dmytro Chornobryvets, Oleh Bakaiev
The accelerated growth of freelance platforms has brought to light several systemic challenges, such as elevated transaction costs, increased susceptibility to fraud, limited transparency, and inefficiencies in the selection of service providers. This study presents the design and implementation of an AI-powered platform aimed at improving the management and monitoring of freelance services. The platform architecture incorporates a multi-criteria risk assessment framework, which evaluates users based on their ratings, transaction history, account longevity, and digital wallet balance. To address issues of contractor reliability and operational anomalies, the system integrates advanced algorithms for automated selection and anomaly detection. A smart contract mechanism, implemented in Solidity and deployed on the Ethereum blockchain via Web3.js, ensures secure and verifiable transactions. For data storage and retrieval, the platform leverages PostgreSQL and MongoDB, while ECDSA cryptographic techniques are employed to reinforce transaction integrity and user authentication. Empirical evaluation indicates that the platform substantially mitigates fraud risks and enhances the efficiency and transparency of interactions between clients and freelancers. The proposed solution demonstrates the potential to support secure and scalable freelance operations and may be extended for deployment within decentralized finance ecosystems and digital commerce environments.
Blockchain technology has created a major transformation in the digital world with features such as decentralization, security, transparency, and traceability. Emerging with Nakamoto's introduction of Bitcoin in 2009, this technology finds applications in various sectors, primarily finance, supply chain, healthcare, and public services. The fundamental principles of blockchain are based on elements such as hashing, distributed ledger technology (DLT), and consensus mechanisms. Innovative technologies such as smart contracts and side chains increase the potential of this system. Cryptocurrencies are an application area of blockchain technology and come across different types such as bitcoin, altcoins, and NFTs. APIs facilitate software development processes and enable integrating systems with blockchain. In the investigative phases, blockchain traceability provides a significant advantage in solving crimes like crypto fraud. This study aims to automate the manual tracking of crypto-related criminal transactions by implementing an algorithm based on the FIFO principle.
This policy paper reviews the evolution of research from 2016 to 2024 on the role of digital assets in illicit financial flows and evaluates the effectiveness of existing detection and prevention strategies. It highlights two central policy imperatives for Latin America: (i) fostering regional cooperation among central banks to harmonize anti-money-laundering standards and address regulatory arbitrage, and (ii) integrating supervisory technologies (SupTech and RegTech) into compliance frameworks to improve detection capacity. The paper concludes that combining technological innovation with coordinated regional regulation is essential to safeguard financial integrity in the digital-asset era.
Stefan Kitzler, Masarah Paquet-Clouston, Bernhard Haslhofer
The Decentralized Finance (DeFi) ecosystem has experienced over \$10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55% of crime events lead to significant negative price impacts, with an average decline of about 14%. Additionally, 68% of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74% of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.
Objective: This research aims to develop a comprehensive framework to identify and prevent money laundering in Decentralized Finance (DeFi) by leveraging big data analytics, integrating advanced machine learning algorithms, and network analysis techniques to address the challenges of pseudonymity and decentralization inherent to this ecosystem. Research Design & Methods: This research utilizes a mixed method approach with machine learning analysis based on Elliptic Dataset and qualitative policy study, applying graph models and classification algorithms to detect illegal transactions with precision in the context of imbalanced data. Findings: The results show that the MLP and GCN models achieve high accuracy (98% and 97.3%) and excellent recall (99.5% and 99.4%) on the Elliptic Dataset, significantly outperforming traditional methods. Exploratory data analysis and graph visualization confirmed that illegal transactions form denser clusters and more complex paths, indicating a layering pattern. Implications and Recommendations: Theoretically, this research extends the application of big data and graph theory to new financial systems, providing a blueprint for future RegTech and FinTech research. Practically, the framework offers tangible tools for regulators, law enforcement, and DeFi platforms to enhance AML capabilities, supporting the development of real-time monitoring tools and risk assessment models. Contribution and Value Added: The main contribution of this research is the development of a robust and adaptive big data analytics-based AML framework, which effectively addresses the unique challenges of DeFi.
The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem's trustless, permissionless, and borderless nature presents substantial regulatory challenges. The absence of centralized oversight and the technical complexity create fertile ground for financial crimes. Among these, money laundering is particularly concerning, as in the event of successful scams, code exploits, and market manipulations, it facilitates covert movement of illicit gains. Beyond this, there is a growing concern that cryptocurrencies can be leveraged to launder proceeds from drug trafficking, or to transfer funds linked to terrorism financing. This survey aims to outline a taxonomy of high-level strategies and underlying mechanisms exploited to facilitate money laundering in Web3. We examine how criminals leverage the pseudonymous nature of Web3, alongside weak regulatory frameworks, to obscure illicit financial activities. Our study seeks to bridge existing knowledge gaps on laundering schemes, identify open challenges in the detection and prevention of such activities, and propose future research directions to foster a more transparent Web3 financial ecosystem -- offering valuable insights for researchers, policymakers, and industry practitioners.
Ubrzani razvoj blockchain tehnologije i ulazak novih kriptovaluta na tržište otvorili su mogućnost primjene kripto tehnologije u različitim industrijama, uključujući turizam i hotelijerstvo. U ovom radu analiziran je potencijal implementacije kriptovaluta u hotelskoj industriji s naglaskom na korištenje kriptovaluta kao sredstva plaćanja. Teorijski dio rada pruža pregled temeljnih značajki kriptovaluta, globalnih trendova u njihovoj upotrebi te prednosti i izazova njihove primjene u hotelijerstvu. Nadalje, analiziraju se globalni trendovi upotrebe kriptovaluta, posebno rast broja korisnika i sve veća prihvaćenost digitalne imovine u platnom prometu, ali i ograničenja kao što su volatilnost te različite regulatorne prakse koje otežavaju njihovu širu primjenu. Kroz modele prihvaćanja tehnologije objašnjava se kako percipirana korisnost, jednostavnost korištenja, tehnološka osviještenost i povjerenje u sustav utječu na namjeru korištenja kripto plaćanja. Empirijski dio temelji se na anketnom istraživanju provedenom među građanima Republike Hrvatske. Anketa je ispitala razinu poznavanja kripto tehnologije, posjedovanja kriptovaluta te namjere korištenja za plaćanje hotelskih usluga kako bi se ocijenilo što će biti potrebno za uspješnu implementaciju u hotelijerskom sektoru te mogu li se uvođenjem ove tehnologije ostvariti konkurentske prednosti.
This article explores the transformative potential of Blockchain and distributed ledger technologies (DLT) in Afghanistan’s financial sector, amid a backdrop of systemic instability, infrastructural gaps, and geopolitical constraints. Drawing on an extensive review of digital banking development, expert interviews, and comparative global experiences, the study critically assesses Afghanistan's readiness to adopt Blockchain as a tool for financial inclusion, transparency, and institutional resilience. Although the formal banking system has largely regressed post-2021, grassroots crypto adoption reflects a latent readiness for decentralized solutions. The paper argues for a strategic, phased approach to Blockchain integration through regulatory reform, stakeholder engagement, and pilot implementations, particularly in land registration and remittance processing.
Digital payments have grown exponentially but face risks such as fraud, account takeover, and unauthorized transactions. This paper explores how blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, can secure digital payments and prevent fraud. We propose a permissioned blockchain architecture for payment systems, integrating identity management, escrow-based smart contracts, and audit-ready transaction logs. Illustrative simulations compare fraud-risk indices, transaction confirmation time, and per-transaction cost across traditional payment gateways and blockchain systems. The results indicate that blockchain can reduce fraud exposure while maintaining near real-time settlement. Challenges such as scalability, privacy, and regulatory compliance are also discussed. This study highlights blockchain?s potential as a preventive, secure mechanism for digital payments and sets the stage for future research integrating zero-knowledge proofs and federated learning.
The quickly advancing development of artificial intelligence (AI) and rapidly spreading cryptocurrencies established a modern environment for both legal developments and illegal practices. The analysis explores AI-related crime activities in cryptocurrencies through a study of AI technologies that assist and combat various crypto-based criminal operations. This paper investigates AI-driven crimes which include fraud alongside money laundering and cyberattacks and evaluates the dual capabilities of AI between criminal execution and prevention functions. This paper explains the ethical and legal risks of AI implementations in cryptocurrency crimes and provides recommendations for present and future research to tackle emerging threats.
Abstract By converting between currencies, cryptocurrency exchanges provide access between the traditional and cryptocurrency ecosystem, making them susceptible to money laundering. The European Union extended the scope of the 5 $$^{\text {th}}$$ Anti-Money Laundering Directive (AMLD5) to include cryptocurrency exchanges, requiring them to obtain a registration, conduct customer due diligence, and report unusual transactions. It is, however, unknown whether the measures introduced by the implementation of AMLD5 lead to less risk exposure and what impact it has on cryptocurrency exchanges. This paper uses a mixed-methods approach to explore the effects of the Dutch implementation of AMLD5 measures on cryptocurrency exchanges active in the Netherlands. We analyzed over 335,000 transactions and complemented them with seven qualitative interviews with Dutch cryptocurrency exchanges and the supervisory authority. We find that the Dutch implementation of AMLD5 imposed high administrative burdens and substantial fees on relatively small exchanges that do not pose high money laundering risks. This raises questions about the alignment of the goals and consequences of the regulation.
Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an across-graph corresponding contract-graph method for vulnerability detection: abstract syntax, control flow, and data flow are fused into a typed, directed contract-graph whose nodes are enriched with pre-code embeddings (GraphCodeBERT or CodeT5+). A Graph Matching Network (GMN) with cross-graph attention compares contract-graphs, aligns homologous sub-graphs associated with vulnerabilities, and supports the interpretation of statements at the level of balance between a broad structural coverage and a discriminative pairwise alignment. The evaluation follows a deployment-oriented protocol with thresholds fixed for validation, multi-seed averaging, and a conservative estimate of sensitivity under low-false-positive budgets. On SmartBugs Wild, the method consistently and markedly exceeds strong rule-based and learning baselines and maintains a higher sensitivity to matching false-positive rates; ablations track the gains to multi-graph fusion, pre-trained encoders, and cross-graph matching, stable through seeds.