Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).
IâFang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contractsâ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMsâ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMsâ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
Abstract This study explores the intersection between cryptocurrencies and criminal organizations through a systematic literature review. By examining peer-reviewed academic publications, the research identifies the mechanisms by which criminal groups leverage digital currencies for illicit activities such as money laundering, fraud, and extortion. A structured PRISMA-based methodology was adopted, employing well-defined inclusion, and exclusion criteria to ensure transparency and replicability. The study reveals critical trends in the misuse of cryptocurrencies, the technological challenges faced by regulators, and the limitations of current enforcement frameworks. The originality of this research lies in synthesizing existing academic insights while identifying key gaps in the literature. The findings highlight the urgent need for tailored regulatory responses and greater cross-border cooperation, contributing to both academic understanding and policymaking. The practical implications include recommendations for policy adaptation and future research pathways to mitigate cryptocurrency-enabled criminal behavior.
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.
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.
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 trĆŸiĆĄte otvorili su moguÄnost primjene kripto tehnologije u razliÄitim industrijama, ukljuÄujuÄi turizam i hotelijerstvo. U ovom radu analiziran je potencijal implementacije kriptovaluta u hotelskoj industriji s naglaskom na koriĆĄtenje kriptovaluta kao sredstva plaÄanja. Teorijski dio rada pruĆŸa pregled temeljnih znaÄajki 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 veÄa prihvaÄenost digitalne imovine u platnom prometu, ali i ograniÄenja kao ĆĄto su volatilnost te razliÄite regulatorne prakse koje oteĆŸavaju njihovu ĆĄiru primjenu. Kroz modele prihvaÄanja tehnologije objaĆĄnjava se kako percipirana korisnost, jednostavnost koriĆĄtenja, tehnoloĆĄka osvijeĆĄtenost i povjerenje u sustav utjeÄu na namjeru koriĆĄtenja kripto plaÄanja. Empirijski dio temelji se na anketnom istraĆŸivanju provedenom meÄu graÄanima Republike Hrvatske. Anketa je ispitala razinu poznavanja kripto tehnologije, posjedovanja kriptovaluta te namjere koriĆĄtenja za plaÄanje hotelskih usluga kako bi se ocijenilo ĆĄto Äe biti potrebno za uspjeĆĄnu implementaciju u hotelijerskom sektoru te mogu li se uvoÄenjem ove tehnologije ostvariti konkurentske prednosti.
This article explores the transformative potential of Blockchain and distributed ledger technologies (DLT) in Afghanistanâs 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.
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.
Recep Arslan, Mustafa Ăzseven, Metin Mutlu Aydın
European cybersecurity is rapidly evolving to address complex and emerging threats fueled by advancements in technology. AI-powered threat analysis has become a cornerstone, enabling faster detection of anomalies, predictive threat modeling, and real-time incident response. As Europe enters the quantum age, cybersecurity strategies are increasingly focused on quantum-resistant encryption to protect critical infrastructure and sensitive data from future quantum attacks. Simultaneously, the rise of blockchain technologies and cryptocurrencies introduces new vulnerabilities, such as smart contract exploits and decentralized finance (DeFi) fraud, requiring targeted regulatory oversight. In response, the EU is strengthening its regulatory frameworks, such as the NIS2 Directive and the Digital Operational Resilience Act (DORA), to ensure a harmonized, proactive approach to cybersecurity governance, resilience, and accountability across sectors. This multifaceted strategy reflects Europeâs commitment to safeguarding digital sovereignty and fostering trust in its digital ecosystem. The study deals with the transformation of the European cyber security ecosystem within the framework of artificial intelligence (AI) supported threat analysis. The paper discusses the security risks that arise in the quantum and post-quantum era, the possibility of blockchain/crypto systems being broken by quantum computers, the limitations of the existing data set, and the need for human-like thinking skills. In addition, the European Union's (EU) cybersecurity policies, data privacy principles, ethical standards, transparency, accountability, and human-centered AI design approaches are examined within the scope of the EU's global norm-setting role. This article also aims to shed light on the strategic steps that will shape the future of AI-powered cyber defense. Study shows that Europe should develop artificial intelligence (AI)-powered cybersecurity solutions in its preparations for the post-quantum era, it also should invest in AI models that transcend current data set limits and have humanoid thinking capacities.
Ioannis Sfyrakis, Paolo Modesti, Lewis Golightly, Minaro Ikegima
Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugsâ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy.
A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de anĂĄlise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisĂŁo de detecção (de 41,7% para 24,3%), levantando dĂșvidas sobre o real progresso das ferramentas.
The success of terrorist organizations in maintaining traditional resources to finance terrorist operations is sufficient to push them away from virtual currencies and their usual risks, as long as they are able to sell oil and transfer funds between their territories, and as long as their funds remain safe from attacks and persecution by the international community. Encrypted virtual currencies are characterized by high degrees of secrecy, privacy, and decentralization - and extremist religious groups And terrorism that adopts violence as a means of operation and expansion, and studying indicators indicating the growing importance of these currencies in circulation, exchange, and commercial transactions, nd in financing extremist religious groups and organizations, and financing the purchase of weapons and equipment used by these groups. It is a decentralized currency with no competent authority, and no central bank responsible for issuing it, and it is not subject to the restrictions of international banking and monetary institutions. This is a significant advantage that has attracted many individuals and groups to its circulation. Had international institutions and organizations been able to subject this currency to international oversight, or to a central authority, it would have lost its most important advantage, and terrorist and extremist organizations would have been unable to exploit it further.
This paper analyzes the risks posed by future-state quantum computers, specifically the âharvest now decrypt laterâ (HNDL) risk. We review foundational concepts of quantum computing to address the present and ongoing threat of HNDL to currently protected data. We use the Bitcoin network as an illustrative example to study the implications of HNDL for distributed ledger cryptocurrency networks that rely upon traditional cryptography. We posit that while cryptocurrency distributed ledger network maintainers could successfully deploy post-quantum cryptography (PQC) mitigations to protect the networkâs security and data integrity against a future-state quantum computer, data privacy of the networkâs previously recorded transactions remains vulnerable against a future-state quantum computer due to HNDL. The difficulty in protecting data privacy lies in the risk that a bad actor can obtain a distributed ledger replica, harvest the data, and in the fullness of time reveal previously obfuscated and confidential data using a sufficiently powerful quantum computer. The authors highlight this gap in data privacy protection and note the shortage of mitigations for the data privacy risks associated with the HNDL threat within distributed ledger networks.
Maria Dona Kristina Wati, Ainul Azizah, Fanny Tanuwijaya
The rapid development of digital technology has transformed the global financial landscape with the emergence of virtual currencies such as Bitcoin. As a decentralized digital asset based on blockchain technology, Bitcoin enables fast, anonymous, and cross-border transactions without central authority intervention. While these features offer convenience, they also pose risks of abuse in illegal activities, including narcotics transactions. In Indonesia, the use of Bitcoin in drug-related offenses presents complex legal challenges due to the absence of explicit criminal regulations governing such usage. This study examines the criminal liability of narcotics offenders who utilize Bitcoin, focusing on three core issues: the juridical basis for punishment, the characteristics of punishment under Indonesiaâs legal system, and an ideal penal concept for the future. The research employs a normative juridical method with statutory, conceptual, and case study approaches. Findings indicate that punishment remains based on Law No. 35 of 2009 on Narcotics, with Bitcoin regarded as a means rather than a core element of the crime. Although it does not constitute a new offense, its use may aggravate punishment through individualized sentencing. Sanctions involving digital assets remain suboptimal due to regulatory gaps. Therefore, this study recommends revising criminal laws to explicitly regulate Bitcoin use in drug crimes, developing sentencing guidelines that consider technological factors, and formulating responsive penal policies, including asset seizure and inter-agency collaboration through blockchain forensic technology.
Decentralized Finance (DeFi) represents the new generation of blockchain financial services by developing an open-access financial model without banking or lending institution intermediaries. However, DeFi's open feature threatens its security, making it vulnerable and a target for different attack types. In this systematic review, we present the security of DeFi by selecting fifteen studies from 2020 to 2024 to determine and display the security solutions' effectiveness in identifying the attacks, focusing on various DeFi components such as smart contracts, DEX, AMM, governance, AMM-based DEX, and smart contracts with (DEX, Oracle); detecting different kinds of attacks (e.g., price manipulation, Oracle manipulation, flash loan) using detection tools (e.g., DeFort, CRPWarner, FORAY); we find out that 40% of the selected studies focus on Oracle manipulation attack, 33.33% for price manipulation and flash loan attacks separately, followed by 13.33% for (MEV, rug pull, front-running, Token Leakage, and deep logical bugs), 6.67% for (EEV, reentrancy, sandwich, access control, and state derailment defects). We compare the studies based on the attack type that they detected using four state-of-the-art types of research, such as DeFiScope, FlashSyn, SecPLF, and DeFiGuard; this indicates the concentration of the trend studies is on accuracy and combining AI in DeFi security, or aggregating the existing tools with it, giving an overview of DeFi components' security, underlining the gaps in the attack types that future research can address to build more robust, trustworthy, and secure DeFi systems.