Smart contract upgrades are increasingly common due to their flexibility in modifying deployed contracts, such as fixing bugs or adding new functionalities. Meanwhile, upgrades compromise the immutability of contracts, introducing significant security concerns. While existing research has explored the security impacts of contract upgrades, these studies are limited in collection of upgrade behaviors and identification of insecurities. To address these limitations, we conduct a comprehensive study on the insecurities of upgrade behaviors. First, we build a dataset containing 83,085 upgraded contracts and 20,902 upgrade chains. To our knowledge, this is the first large-scale dataset about upgrade behaviors, revealing their diversity and exposing gaps in public disclosure. Next, we develop a taxonomy of insecurities based on 37 real-world security incidents, categorizing eight types of upgrade risks and providing the first complete view of upgrade-related insecurities. Finally, we survey public awareness of these risks and existing mitigations. Our findings show that four types of security risks are overlooked by the public and lack mitigation measures. We detect these upgrade risks through a preliminary study, identifying 31,407 related issues - a finding that raises significant concerns.
Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union's Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong's Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure.
The increasing penetration of renewable energy sources in day-ahead energy markets introduces challenges in balancing supply and demand, ensuring grid resilience, and maintaining trust in decentralized trading systems. This paper proposes a novel framework that integrates the Proximal Policy Optimization (PPO) algorithm, a state-of-the-art reinforcement learning method, with blockchain technology to optimize automated trading strategies for prosumers in day-ahead energy markets. We introduce a comprehensive framework that employs RL agent for multi-objective energy optimization and blockchain for tamper-proof data and transaction management. Simulations using real-world data from the Electricity Reliability Council of Texas (ERCOT) demonstrate the effectiveness of our approach. The RL agent achieves demand-supply balancing within 2\% and maintains near-optimal supply costs for the majority of the operating hours. Moreover, it generates robust battery storage policies capable of handling variability in solar and wind generation. All decisions are recorded on an Algorand-based blockchain, ensuring transparency, auditability, and security - key enablers for trustworthy multi-agent energy trading. Our contributions include a novel system architecture, curriculum learning for robust agent development, and actionable policy insights for practical deployment.
Academic publishing, integral to knowledge dissemination and scientific advancement, increasingly faces threats from unethical practices such as unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest. While existing infrastructures like ORCID effectively disambiguate researcher identities, they fall short in enforcing explicit authorship consent, accurately verifying contributor roles, and robustly detecting conflicts of interest during peer review. To address these shortcomings, this paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology. The proposed model uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to securely verify author identities and contributions, reducing ambiguity and ensuring accurate attribution. A blockchain-based trust registry records authorship consent and peer-review activity immutably. Privacy-preserving cryptographic techniques, especially Zero-Knowledge Proofs (ZKPs), support conflict-of-interest detection without revealing sensitive data. Verified authorship metadata and consent records are embedded in publications, increasing transparency. A stakeholder survey of researchers, editors, and reviewers suggests the framework improves ethical compliance and confidence in scholarly communication. This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
Mirza Ahad Baig, Christoph U. Günther, Krzysztof Pietrzak
The blocks in the Bitcoin blockchain record the amount of work W that went into creating them through proofs of work. When honest parties control a majority of the work, consensus is achieved by picking the chain with the highest recorded weight. Resources other than work have been considered to secure such longest-chain blockchains. In Chia, blocks record the amount of space S (via a proof of space) and sequential computational steps V (via a VDF). In this paper, we ask what weight functions Γ(S,V,W) (that assign a weight to a block as a function of the recorded space, speed, and work) are secure in the sense that whenever the weight of the resources controlled by honest parties is larger than the weight of adversarial parties, the blockchain is secure against private double-spending attacks. We completely classify such functions in an idealized "continuous" model: Γ(S,V,W) is secure against private double-spending attacks if and only if it is homogeneous of degree one in the timed resources V and W, i.e., αΓ(S,V,W)=Γ(S,αV, αW). This includes Bitcoin rule Γ(S,V,W)=W and Chia rule Γ(S,V,W) = SV. In a more realistic model where blocks are created at discrete time-points, one additionally needs some mild assumptions on the dependency on S (basically, the weight should not grow too much if S is slightly increased, say linear as in Chia). Our classification is more general and allows various instantiations of the same resource. It provides a powerful tool for designing new longest-chain blockchains. E.g., consider combining different PoWs to counter centralization, say the Bitcoin PoW W_1 and a memory-hard PoW W_2. Previous work suggested to use W_1+W_2 as weight. Our results show that using {\sqrt}(W_1){\cdot}{\sqrt}(W_2), {\min}{W_1,W_2} are also secure, and we argue that in practice these are much better choices.
Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart contract vulnerability detection, leaving end-to-end automated exploit generation (AEG) much less understood. We study that gap with \textsc{ReX}, an execution-grounded framework that links LLM-based exploit synthesis to the Foundry stack for end-to-end generation, compilation, execution, and validation. Five recent LLMs are evaluated across eight common vulnerability classes, supported by a curated dataset of 38{+} real incident PoCs and three automation aids: prompt refactoring, a compiler feedback loop, and templated test harnesses. Results indicate that current frontier LLMs can often produce deterministic PoCs for single-contract vulnerabilities, but remain weak on cross-contract attacks; outcomes depend mainly on the model and bug type, while code structure and prompt tuning contribute less in our setting. The study also surfaces important boundary conditions of LLM-driven AEG, including gaps between oracle-validated exploitability and real-world economic attacks, pointing to the need for stronger defenses and more realistic evaluation.
The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls, leading to exploit entry points such as flash loan attacks and reentrancy attacks. In this paper, we propose UEChecker, a deep learning-based tool that utilizes a call graph and a Graph Convolutional Network to detect unchecked external call vulnerabilities. We design the following components: An edge prediction module that reconstructs the feature representation of nodes and edges in the call graph; A node aggregation module that captures structural information from both the node itself and its neighbors, thereby enhancing feature representation between nodes and improving the model's understanding of the global graph structure; A Conformer Block module that integrates multi-head attention, convolutional modules, and feedforward neural networks to more effectively capture dependencies of different scales within the call graph, extending beyond immediate neighbors and enhancing the performance of vulnerability detection. Finally, we combine these modules with Graph Convolutional Network to detect unchecked external call vulnerabilities. By auditing the smart contracts of 608 DApps, our results show that our tool achieves an accuracy of 87.59% in detecting unchecked external call vulnerabilities. Furthermore, we compare our tool with GAT, LSTM, and GCN baselines, and in the comparison experiments, UEChecker consistently outperforms these models in terms of accuracy.
The rapid adoption of agentic AI, powered by large language models (LLMs), is transforming enterprise ecosystems with autonomous agents that execute complex workflows. Yet we observe several key security vulnerabilities in LLM-driven multi-agent systems (MASes): fragmented identity frameworks, insecure communication channels, and inadequate defenses against Byzantine agents or adversarial prompts. In this paper, we present the first systematic analysis of these emerging multi-agent risks and explain why the legacy security strategies cannot effectively address these risks. Afterwards, we propose BlockA2A, the first unified multi-agent trust framework that enables secure and verifiable and agent-to-agent interoperability. At a high level, BlockA2A adopts decentralized identifiers (DIDs) to enable fine-grained cross-domain agent authentication, blockchain-anchored ledgers to enable immutable auditability, and smart contracts to dynamically enforce context-aware access control policies. BlockA2A eliminates centralized trust bottlenecks, ensures message authenticity and execution integrity, and guarantees accountability across agent interactions. Furthermore, we propose a Defense Orchestration Engine (DOE) that actively neutralizes attacks through real-time mechanisms, including Byzantine agent flagging, reactive execution halting, and instant permission revocation. Empirical evaluations demonstrate BlockA2A's effectiveness in neutralizing prompt-based, communication-based, behavioral and systemic MAS attacks. We formalize its integration into existing MAS and showcase a practical implementation for Google's A2A protocol. Experiments confirm that BlockA2A and DOE operate with sub-second overhead, enabling scalable deployment in production LLM-based MAS environments.
As an emerging service framework built by combining cryptography, P2P network, consensus mechanism and innovative contract technology, blockchain has been widely used in digital finance, data sharing, message traceability and electronic evidence preservation because of its decentralised, non-tamperable and transaction traceability. However, with the complex and changeable application scenarios of blockchain technology and the continuous enhancement of blockchain attack technology, the security of the blockchain system has been seriously threatened, dramatically affecting the development and application of blockchain technology. This paper aims to analyse the attacks on blockchain from the perspective of cryptography. Firstly, from the cryptography technology in the blockchain, the principle of hash functions, digital signatures, and other technologies, as well as their role in the blockchain, are introduced. Then, based on the six-layer architecture of the blockchain, the attacks on the data layer, the network layer, the consensus layer, the contract layer, the incentive layer and the application layer are analysed, and the methods to mitigate or resist the attacks are proposed. Secondly, the attack principles of 51% attack, Double-Spending attack, Reentrancy attack, Replay attack, Sybil attack and Timestamp Tampering attack were analysed, and the mitigation or defence solutions for these six attacks were designed. Finally, the core problems to be solved in blockchain technology are summarised, and the future development of blockchain security technology is projected.
The introduction of smart contract functionality marks the advent of the blockchain 2.0 era, enabling blockchain technology to support digital currency transactions and complex distributed applications. However, many smart contracts have been found to contain vulnerabilities and errors, leading to the loss of assets within the blockchain. Despite a range of tools that have been developed to identify vulnerabilities in smart contracts at the source code or bytecode level, most rely on a single modality, reducing performance, accuracy, and limited generalization capabilities. This paper proposes a multimodal deep learning approach, MultiCFV, which is designed specifically to analyze and detect erroneous control flow vulnerability, as well as identify code clones in smart contracts. Bytecode is generated from source code to construct control flow graphs, with graph embedding techniques extracting graph features. Abstract syntax trees are used to obtain syntax features, while code comments capture key commentary words and comment features. These three feature vectors are fused to create a database for code inspection, which is used to detect similar code and identify contract vulnerabilities. Experimental results demonstrate our method effectively combines structural, syntactic, and semantic information, improving the accuracy of smart contract vulnerability detection and clone detection.
Security issues are becoming increasingly significant with the rapid evolution of Non-fungible Tokens (NFTs). As NFTs are traded as digital assets, they have emerged as prime targets for cyber attackers. In the development of NFT smart contracts, there may exist undiscovered defects that could lead to substantial financial losses if exploited. To tackle this issue, this paper presents a framework called NATLM(NFT Assistant LLM), designed to detect potential defects in NFT smart contracts. The framework effectively identifies four common types of vulnerabilities in NFT smart contracts: ERC-721 Reentrancy, Public Burn, Risky Mutable Proxy, and Unlimited Minting. Relying exclusively on large language models (LLMs) for defect detection can lead to a high false-positive rate. To enhance detection performance, NATLM integrates static analysis with LLMs, specifically Gemini Pro 1.5. Initially, NATLM employs static analysis to extract structural, syntactic, and execution flow information from the code, represented through Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). These extracted features are then combined with vectors of known defect examples to create a matrix for input into the knowledge base. Subsequently, the feature vectors and code vectors of the analyzed contract are compared with the contents of the knowledge base. Finally, the LLM performs deep semantic analysis to enhance detection capabilities, providing a more comprehensive and accurate identification of potential security issues. Experimental results indicate that NATLM analyzed 8,672 collected NFT smart contracts, achieving an overall precision of 87.72%, a recall of 89.58%, and an F1 score of 88.94%. The results outperform other baseline experiments, successfully identifying four common types of defects.
Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.
Kiana Kiashemshaki, Elvis Nnaemeka Chukwuani, Mohammad Jalili Torkamani, Negin Mahmoudi
Blockchain technology offers a promising foundation for modernizing E-Voting systems by enhancing transparency, decentralization, and security. Yet, real-world adoption remains limited due to persistent challenges such as scalability constraints, high computational demands, and complex privacy requirements. This paper presents a comparative framework for analyzing blockchain-based E-Voting architectures, consensus mechanisms, and cryptographic protocols. We examine the limitations of prevalent models like Proof of Work, Proof of Stake, and Delegated Proof of Stake, and propose optimization strategies that include hybrid consensus, lightweight cryptography, and decentralized identity management. Additionally, we explore the novel role of Large Language Models (LLMs) in smart contract generation, anomaly detection, and user interaction. Our findings offer a foundation for designing secure, scalable, and intelligent blockchain-based E-Voting systems suitable for national-scale deployment. This work lays the groundwork for building an end-to-end blockchain E-Voting prototype enhanced by LLM-guided smart contract generation and validation, supported by a systematic framework and simulation-based analysis.
Due to the high flexibility and versatility, unmanned aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue.However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39$\%$, 12.74$\%$, and 16.6$\%$ than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.
Blockchain technology is widely used in various fields due to its ability to provide decentralization and trustless security. This is a fundamental understanding held by many advocates, but it is misunderstood, leading participants to fail to recognize the limitations of the security that blockchain can provide. Among all current network attacks, Denial of Service (DoS) attacks pose significant threats due to their ease of execution and destructive potential. This paper, based on the blockchain architecture hierarchy, categorizes and organizes existing DoS attacks, with a focus on explaining the principles and methods of contract layer and consensus layer DoS attacks. Furthermore, this paper comprehensively analyzes and compares commonly used detection methods and defense technologies, which will contribute to strengthening the security and stability of blockchain systems and promoting further innovation and application of blockchain systems.
With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1) Static analysis methods struggle with complex scenarios. 2) Methods based on specialized pre-trained models perform well on specific datasets but have limited generalization capabilities. In contrast, general-purpose Large Language Models (LLMs) demonstrate impressive ability in adapting to new vulnerability patterns. However, they often underperform on specific vulnerability types compared to methods based on specialized pre-trained models. We also observe that explanations generated by generalpurpose LLMs can provide fine-grained code understanding information, contributing to improved detection performance. Inspired by these observations, we propose SAEL, a LLMbased framework for smart contract vulnerability detection. First, we design prompts targeting specific smart contract vulnerabilities to guide general-purpose LLMs in detecting vulnerabilities and providing explanations. The detection results generated by LLMs serve as prediction features. Then, we employ prompt-tuning on CodeT5 and T5 respectively to process contract code and explanations, enhancing model performance on specific tasks. To leverage the strengths of each component, we introduce Adaptive Mixture-of-Experts, a dynamic architecture for smart contract vulnerability detection. This mechanism dynamically adjusts feature weights through a Gating Network, which selects the most relevant features by applying TopK filtering and Softmax normalization, and a Multi-Head Self-Attention mechanism, which enhances cross-feature relationships by processing multiple attention heads in parallel. This design ensures that prediction results for LLMs, explanation features, and contract code features are effectively integrated through gradient optimization. The loss function focuses on the independent prediction performance of each feature and the overall performance of weighted predictions. Experimental results show that SAEL outperforms existing methods in detecting various vulnerabilities.
Ethan Buchman, Paolo Dini, Shoaib Ahmed, Andrew Miller · 5 authors
For centuries, financial institutions have responded to liquidity challenges by forming closed, centralized clearing clubs with strict rules and membership that allow them to collaborate on using the least money to discharge the most debt. As closed clubs, much of the general public has been excluded from participation. But the vast majority of private sector actors consists of micro or small firms that are vulnerable to late payments and generally ineligible for bank loans. This low liquidity environment often results in gridlock and leads to insolvency, and it disproportionately impacts small enterprises and communities. On the other hand, blockchain communities have developed open, decentralized settlement systems, along with a proliferation of store of value assets and new lending protocols, allowing anyone to permissionlessly transact and access credit. However, these protocols remain used primarily for speculative purposes, and so far have fallen short of the large-scale positive impact on the real economy prophesied by their promoters. We address these challenges by introducing Cycles, an open, decentralized clearing, settlement, and issuance protocol. Cycles is designed to enable firms to overcome payment inefficiencies, to reduce their working capital costs, and to leverage diverse assets and liquidity sources, including cryptocurrencies, stablecoins, and lending protocols, in service of clearing more debt with less money. Cycles solves real world liquidity challenges through a privacy-preserving multilateral settlement platform based on a graph optimization algorithm. The design is based on a core insight: liquidity resides within cycles in the payment network's structure and can be accessed via settlement flows optimized to reduce debt.
Guang Yang, Peter Trinh, Alma Nkemla, Amuru Serikyaku · 6 authors
The current Domain Name System (DNS) infrastructure faces critical vulnerabilities including poisoning attacks, censorship mechanisms, and centralized points of failure that compromise internet freedom and security. Recent incidents such as DNS poisoning attacks on ISP customers highlight the urgent need for resilient alternatives. This paper presents a novel blockchain-based Decentralized Domain Name System (DDNS). We designed a specialized Proof-of-Work blockchain to maximize support for DNS-related protocols and achieve node decentralization. The system integrates our blockchain with IPFS for distributed storage, implements cryptographic primitives for end-to-end trust signatures, and achieves Never Trust, Always Verify zero-trust verification. Our implementation achieves 15-second domain record propagation times, supports 20 standard DNS record types, and provides perpetual free .ddns domains. The system has been deployed across distributed infrastructure in San Jose, Los Angeles, and Orange County, demonstrating practical scalability and resistance to traditional DNS manipulation techniques. Performance evaluation shows the system can handle up to Max Theor. TPS 1,111.1 tx/s (minimal transactions) and Max Theor. TPS 266.7 tx/s (regular transactions) for domain operations while maintaining sub-second query resolution through intelligent caching mechanisms.
This paper presents the development of a distributed digital identity system utilizing modern technologies, including FastAPI, MongoDB, gRPC, Docker, and blockchain simulation with Ganache and Ethereum. The objective is to demonstrate the benefits of distributed systems and blockchain for the security, traceability, and decentralization of digital identities. The methodology included the development of a microservices architecture with JWT authentication, data persistence in MongoDB, simulation of blockchain operations using Ganache, and containerization with Docker. The results demonstrate the feasibility of the proposed approach, with a functional web interface, complete audit logs, and blockchain simulation with Ethereum. The theoretical foundations, technical implementation, results obtained, and prospects for integration with real blockchain networks are discussed.
A widespread belief in the blockchain security community is that automated techniques are only good for detecting shallow bugs, typically of small value. In this paper, we present the techniques and insights that have led us to repeatable success in automatically discovering high-value smart contract vulnerabilities. Our vulnerability disclosures have yielded 10 bug bounties, for a total of over $3M, over high-profile deployed code, as well as hundreds of bugs detected in pre-deployment or under-audit code. We argue that the elements of this surprising success are a) a very high-completeness static analysis approach that manages to maintain acceptable precision; b) domain knowledge, provided by experts or captured via statistical inference. We present novel techniques for automatically inferring domain knowledge from statistical analysis of a large corpus of deployed contracts, as well as discuss insights on the ideal precision and warning rate of a promising vulnerability detector. In contrast to academic literature in program analysis, which routinely expects false-positive rates below 50% for publishable results, we posit that a useful analysis for high-value real-world vulnerabilities will likely flag very few programs (under 1%) and will do so with a high false-positive rate (e.g., 95%, meaning that only one-of-twenty human inspections will yield an exploitable vulnerability).
This paper presents MPC-EVM, the first blockchain prototype that extends the EVM to enable asynchronous MPC invocations by smart contracts during transaction executions without compromising consistency or throughput. MPC-EVM uses an asynchronous execution model to process MPC-invoking transactions in a non-blocking fashion, saving the transaction's progress when it enters an MPC and resuming its execution upon MPC's completion. Additionally, it employs an access control mechanism that prevents inconsistent state access and modifications as a result of asynchronous executions. Benchmarking MPC-EVM's throughput show that the transactions per second (TPS) decreased by less than 3% compared to the baseline when MPC-invoking transactions are executed alongside regular transactions.
Hadis Rezaei, Mojtaba Eshghie, Karl Anderesson, Francesco Palmieri
While catastrophic attacks on Ethereum persist, vulnerability research remains fixated on implementation-level smart contract bugs, creating a gap between academic understanding of vulnerabilities and the root causes of high-impact, real-world incidents. To address this, we employ a two-pronged methodology: first, a systematic literature review of 71 academic papers to build a catalog of 24 active and 5 deprecated vulnerabilities. Second, we conduct an in-depth, empirical analysis of 50 of the most severe real-world attacks between 2022 and 2025, collectively incurring over $1.09B in losses, to identify their root causes. We introduce the concept of "exploit chains" by revealing that many incidents are not caused by isolated vulnerabilities but by combinations of human, operational, and economic design flaws that link with implementation bugs to enable an attack. Our analysis yields insights on how decentralized applications are exploited in practice, leading to a novel, four-tier root-cause framework that moves beyond code-level vulnerabilities. We find that real-world successful attacks on Ethereum (and related networks) trace back to one of the four tiers of (1) protocol logic design, (2) lifecycle and governance, (3) external dependencies, and (4) classic smart contract vulnerabilities. We investigate the suitability of this multi-tier incident root-cause framework via a case study.
Fintech provides technological services to increase operational efficiency in financial institutions, but traditional perimeter-based defense mechanisms are insufficient against evolving cyber threats like insider attacks, malware intrusions, and Advanced Persistent Threats (APTs). These vulnerabilities expose Fintech organizations to significant risks, including financial losses and data breaches. To address these challenges, this paper proposes a blockchain-integrated Zero Trust framework, adhering to the principle of "Never Trust, Always Verify." The framework uses Ethereum smart contracts to enforce Multi Factor Authentication (MFA), Role-Based Access Control (RBAC), and Just-In-Time (JIT) access privileges, effectively mitigating credential theft and insider threats, the effect of malware and APT attacks. The proposed solution transforms blockchain into a Policy Engine (PE) and Policy Enforcement Point (PEP), and policy storage, ensuring immutable access control and micro-segmentation. A decentralized application (DApp) prototype was developed and tested using STRIDE threat modeling, demonstrating resilience against spoofing, tampering, and privilege escalation. Comparative analysis with Perimeter-based systems revealed a trade-off: while the framework introduced a marginal latency increase (74.0 ms vs. 49.33 ms) and reduced throughput (30.77 vs. 50.0 requests/sec), it significantly enhanced security by eliminating single points of failure and enabling tamper-proof audit trails. Experimental validation on a 200-node simulated network confirmed the framework's robustness, with future optimizations targeting Layer-2 solutions for scalability. This work bridges the gap between Zero Trust theory and practical blockchain implementation, offering Fintech organizations a decentralized, cost-effective security model.
Ali RajabiNekoo, Laleh Rasoul, Amirfarhad Farhadi, Azadeh Zamanifar
Traditional methods for identifying impactful liquidity providers (LPs) in Concentrated Liquidity Market Makers (CLMMs) rely on broad measures, such as nominal capital size or surface-level activity, which often lead to inaccurate risk analysis. The SILS framework offers a significantly more detailed approach, characterizing LPs not just as capital holders but as dynamic systemic agents whose actions directly impact market stability. This represents a fundamental paradigm shift from the static, volume-based analysis to a dynamic, impact-focused understanding. This advanced approach uses on-chain event logs and smart contract execution traces to compute Exponential Time-Weighted Liquidity (ETWL) profiles and apply unsupervised anomaly detection. Most importantly, it defines an LP's functional importance through the Liquidity Stability Impact Score (LSIS), a counterfactual metric that measures the potential degradation of the market if the LP withdraws. This combined approach provides a more detailed and realistic characterization of an LP's impact, moving beyond the binary and often misleading classifications used by existing methods. This impact-focused and comprehensive approach enables SILS to accurately identify high-impact LPs-including those missed by traditional methods and supports essential applications like a protective oracle layer and actionable trader signals, thereby significantly enhancing DeFi ecosystem. The framework provides unprecedented transparency into the underlying liquidity structure and associated risks, effectively reducing the common false positives and uncovering critical false negatives found in traditional models. Therefore, SILS provides an effective mechanism for proactive risk management, transforming how DeFi protocols safeguard their ecosystems against asymmetric liquidity behavior.