Decentralized finance (DeFi) protocols now intermediate over USD 100 billion in value, including regulated stablecoins and tokenized assets deployed as collateral, yet no widely adopted framework operationalizes risk assessment at the rigor institutional adoption demands. Existing approaches emphasize protocol-specific parameter optimization or conceptual taxonomies without providing explainable, composability-aware, and structurally independent assessment methodologies. We propose a nine-dimension DeFi risk assessment framework extending the six-dimension taxonomy introduced by Moody's Analytics and Gauntlet with three novel dimensions: composability risk, comprehension debt, and temporal risk dynamics. We additionally introduce a transparency confidence modifier separating assessment reliability from risk severity. The framework is grounded in structural analysis of protocol dependencies conducted through an ontology-based protocol intelligence infrastructure covering more than 8,000 DeFi protocols. We retrospectively analyze 12 major DeFi-related incidents from 2024-2026 representing approximately USD 2.5 billion in direct losses. Five of the 12 incidents require at least one novel dimension for complete root-cause characterization, including the two highest-systemic-impact events in the dataset.
Three traits of decentralized finance are studied. First, the market impact function is derived for optimal-growth liquidity providers. For a standard random walk, the classic square-root impact is recovered. An extension is then derived to fit general fractional Ornstein-Uhlenbeck processes. These findings break with the linearized liquidity models used in most decentralized exchanges. Second, a Constant Product Market Maker is viewed as a multi-phase Carnot engine, where one phase matches the exchange of tokens by a liquidity taker, and another the change of pool size by a liquidity provider. Third, stablecoin de-pegging is a form of catastrophe risk. By using growth optimization, default odds are linked to the cost of catastrophe bonds. De-pegging insurance can act as a counterweight and a key marketing tool when the law forbids the payment of interest on stablecoins.
Purpose — This paper investigates whether perpetual futures can satisfy Islamic prohibitions on riba (interest), gharar (uncertainty), and maysir (speculation) — a question unaddressed by prior literature. Design/methodology/approach — The study develops a four-category taxonomy of perpetual futures by funding-formula structure, collects 39,406 unique funding-rate intervals across four platforms over 365 days (February 2025 – February 2026), and analyses a cross-platform comparison — with a placebo design — between interest-bearing and interest-free DEX protocols. Shariah analysis applies El-Gamal's riba conditions, Kamali's standardisation criterion, and Salamon's maysir test. Findings — The interest parameter in CEX funding formulas is mathematically unnecessary. dYdX v4 operates with I = 0 and produces a funding distribution distinct from CEX platforms (Cohen's d = 0.782 at native cadence, 0.703 cadence-matched; Kolmogorov-Smirnov D = 0.506). Hyperliquid — a DEX using the CEX formula — clusters with CEX. Formula structure rather than exchange architecture governs the riba dimension of the Shariah classification. Originality/value — The first empirically validated framework demonstrating that the riba component of perpetual futures funding is structurally removable, correcting an error of extrapolation in Islamic finance scholarship that treated a single CEX implementation as representative of the entire instrument class. Ownership (qabdh) and delivery-intent are scoped as open questions for cash-settled synthetic perpetuals. Research limitations/implications — The empirical sample covers a single 365-day period; extension to additional time windows would strengthen external validity. Practical implications — The taxonomy provides a screening tool for Shariah boards, fund managers, and exchange designers evaluating Shariah-compliant perpetual futures instruments.
Insurance governance fundamentally concerns the legitimate authority to price risk, allocate capital, and absorb correlated losses. Traditional models centralize this authority in credentialed institutions, while existing decentralized finance (DeFi) protocols either replicate centralized control or treat critical actuarial parameters as exogenous inputs. This paper resolves the decentralized insurance governance trilemma by proposing the first complete theoretical framework built on a three-tier architecture of capital-backed parameter markets, extending and completing the Risk Coin framework (Yu, 2025). The architecture transforms actuarial assumptions into endogenously discovered economic variables. At the Risk-Pool Tier, insurers commit capital to bid on parameters of an MBBEFD exposure curve. Policyholders signal their private risk assessments by declaring a coverage limit and a total premium payment; from these, an implied retention multiplier is derived after market pricing. At the Catastrophe-Pool Tier, reinsurers likewise commit capital to bid on parameters for pricing optional excess-of-loss reinsurance. The core governance innovation is a recursive design: a Catastrophe Pool operates identically to a Risk Pool, with Risk Pools as its policyholders, thereby unifying the governance logic of primary and reinsurance markets under the same capital-backed mechanism. The ecosystem is anchored by the Risk-Coin-Fund Tier, which centralizes all capital—including the base risk premiums (corresponding to actuarial costs), policyholders’ voluntary supra-actuarial contributions, and professional investment reinsurance capital—and serves as the ultimate residual risk bearer. A core innovation is the consistent application of Risk Coin as a dual-purpose claim on the collective capital pool. RC tokens are issued to participants based on two principles: (1) as compensation for voluntary capital investment (the portion of declared premiums exceeding actuarial cost), and (2) as payment for risk-bearing, with the latter quantified by the market-determined exposure curve 𝐺(⋅). This creates a unified incentive system where capital commitment confers pricing authority, and better risk management preserves RC value. We formalize the mechanism and prove its core properties: incentive-compatible parameter bidding, asymptotic information aggregation, dynamic stability of parameter markets, systemic resilience via an exponential solvency guarantee for diversifiable risks, and regulatory non-intrusiveness. A pivotal theoretical extension is the introduction of a protocol-native capital coverage ratio (ρ), which emerges from the recursive markets. This ratio is governed by a target (𝜌target) and serves as a real-time, transparent solvency signal. It demonstrates how decentralized systems can achieve endogenous stability while providing a direct interface for financial oversight, effectively bridging cryptographic economics with prudential regulatory frameworks. The framework demonstrates that legitimate pricing authority and financial stability can emerge organically from cryptographic economics where tokens serve not as speculative instruments but as verifiable, economically-founded claims on underwriting capacity and capital pool ownership. By solving the governance trilemma through recursive capital-backed parameter markets, a unified RC-based capital accounting system, and a native regulatory interface, this work provides more than a new insurance mechanism—it offers a blueprint for stable, transparent, incentive-aligned, and regulatorily-compatible financial ecosystems that reimagine the foundations of risk-sharing and financial governance.
This paper presents a model in which risk-averse individuals can purchase insurance via traditional indemnity contracts or Decentralized Finance (DeFi) smart contract-based instruments. The model incorporates key features of DeFi insurance, including parametric payouts, basis risk arising from imperfect loss verification and pooled collateralization involving the risk of liquidity shortfalls. We characterize optimal insurance choices as a function of pricing, payout correlation and risk preferences. Numerical results show that DeFi insurance can complement or replace traditional coverage, improving welfare when basis and default risks are moderate or pricing advantages are substantial. The analysis reveals how DeFi-specific frictions shape insurance demand and provides insight into how DeFi instruments may shift market structure and expand the set of attainable risk transfer outcomes.
K. C. Yang, W. P. Chao, Cm Shih, C.C. Kao · 5 authors
With the rapid advancement of technology in the financial sector, financial technology (FinTech) has become a major focus of industry development. Among its various branches, insurance technology (InsurTech) has emerged as a particularly prominent and widely discussed topic in recent years. By leveraging innovative technologies such as artificial intelligence (AI) and blockchain, insurance processes can be automated to enhance service efficiency while maintaining data integrity. This study proposes a business model based on blockchain technology and implements an insurance system using Ethereum smart contracts to support the purchase of flight delay insurance and automate the claims process. The system ensures the integrity of both policy information and transaction records. To determine flight delays, it utilizes the Open API provided by the Transportation Data eXchange (TDX) of Taiwan’s Ministry of Transportation. This integration aligns with the core principles of insurtech, particularly in promoting data sharing and interoperability in system development.
Christopher De Baets, Basem Suleiman, Armin Chitizadeh, Imran Razzak
Abstract In the growing field of blockchain technology, smart contracts exist as transformative digital agreements that execute transactions autonomously in decentralised networks. However, these contracts face challenges in the form of security vulnerabilities, posing significant financial and operational risks. While traditional methods to detect and mitigate vulnerabilities in smart contracts are limited due to a lack of comprehensiveness and effectiveness, integrating advanced machine learning technologies presents an attractive approach to increasing effective vulnerability countermeasures. We endeavour to fill an important gap in the literature by conducting a rigorous systematic review, exploring the intersection between machine learning and smart contracts. Specifically, the study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including K-nearest neighbours, random forest, decision trees, extreme gradient boosting, and support vector machines, outperform static tools in vulnerability detection. Moreover, multi-model approaches integrating deep learning and classical machine learning show significant improvements in precision and recall, while hybrid models employing various techniques achieve near-perfect performance in vulnerability detection accuracy. By integrating state-of-the-art solutions, this work synthesises current methods, thoroughly investigates research gaps, and suggests future directions. The insights gathered are intended to serve as a seminal reference for academics, industry experts, and bodies interested in leveraging machine learning to enhance smart contract security.
Lana AL-Khalaileh, Tareq Al-Billeh, Abdul Salam Al-Findi, Odai Al-Hailat
This study deals with a new technology in contracting, resulting from the information technology (IT) revolution in the field of electronic transactions, which is called “smart contracts”. The latter has constituted a breakthrough in the field of contracting since it provides automation, which underlies many advantages for contractors, so that the software works of smart contracts provide immediate and automatic execution of the contract, which provides speed of implementation and security from manipulation after concluding the contract. So, it provides elements of technical security and trust for this type of contract. This new contractual pattern is considered one of the first in the provisions of Islamic Sharia, which urges us to know the extent of its compatibility with its contracting system. The study concluded with several recommendations, the most significant being that international accords lack comprehensive legislation governing transactions executed through smart contracts. While they contain certain restrictions about contracts formed through contemporary electronic methods, they inadequately elucidate the characteristics of such contracts and examine their specifics. The legal issues associated with smart contracts stem from their connection to digital currency, which is banned by Sharia law.
Oct 10, 2025·Companion Proceedings of the 2025 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity
This proposal presents a multi-layer dynamic security framework for protecting DeFi smart contracts against evolving attack vectors that traditional static analysis and security audits fail to detect. We observed that many DeFi exploits succeed not due to source code bugs, but because of flawed assumptions about user behaviors and external dependencies that only manifest at runtime. Our system provides three complementary additional defenses to significantly increase the difficulty of launching successful attacks: (1) CrossGuard, a control-flow integrity mechanism that only whitelists legitimate invocation patterns;(2) Trace2Inv, a runtime invariant generator that learns and enforces invariants from historical transaction data; and (3) an ecosystem-wide risk analysis tool that detects compositional vulnerabilities in protocol dependencies. By leveraging upgradeable contracts, the framework can progressively refine defenses as protocols stabilize. Our evaluation results show blocking 85% of past exploits with under 1% false positives and below 20% gas overhead.
Abdul Malik, Gayatri Putri, Hesti Putri, Ahmad Badruddin
The proliferation of crypto-assets has raised critical questions about their impact on global financial stability. This study rigorously investigates the structural evolution of the cryptocurrency market's role within the global financial system, testing the hypothesis that it has transitioned from a peripheral, shock-absorbing entity into a systemically significant transmitter of financial risk. We employ a Time-Varying Parameter Vector Autoregression (TVP-VAR) model on daily data from January 1, 2017, to December 31, 2024, examining the dynamic connectedness between a bespoke, rebalanced cryptocurrency index (CRIX20) and key global financial indicators (S&P 500, MSCI World, VIX, DXY). The econometric framework utilizes a Bayesian estimation approach with standard priors, a 200-day rolling window, and a 10-day forecast horizon for Generalized Forecast Error Variance Decompositions (GFEVD). Methodological robustness is confirmed through structural break tests and sensitivity analysis of the forecast horizon. Our findings reveal a profound structural transformation. Prior to mid-2020, the cryptocurrency market was a consistent net receiver of financial spillovers. A structural break, formally identified in the third quarter of 2020, marks a definitive regime shift. Post-break, the crypto market has become a significant and persistent net transmitter of risk to the traditional financial system. The total connectedness index for the entire system shows a marked secular increase, with the crypto market's contribution to systemic risk growing substantially. Gross spillover analysis confirms this shift is driven by a dramatic increase in risk transmission from the crypto market to other assets. In conclusion, the cryptocurrency market can no longer be considered an isolated ecosystem; it is now an integral and potentially destabilizing component of the global financial architecture. The era of crypto-assets as reliable diversifiers has waned, replaced by a new reality where shocks originating within this market pose a credible threat to broader financial stability. These findings present urgent challenges for regulatory oversight, systemic risk monitoring, and portfolio management.
Cha, Jinho, Long Hoang Pham, Thi Quynh Trang Vo, Jaeyoung Cho · 5 authors
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.
As security concerns in blockchain platforms continue to rise, triggered by substantial financial losses, detecting vulnerabilities in smart contracts has emerged as a crucial focus for both academia and industry. Although many promising vulnerability detection methods for Ethereum have been proposed in recent years, their long-term reliability and adaptability across different versions of smart contracts remain unresolved. Specifically, the performance of these methods tends to degrade over time, and in some cases, they may even fail entirely. A key factor contributing to this dilemma is the regular updates of Solidity versions. These updates often introduce new features or syntax changes, which significantly influence how vulnerabilities are manifested and detected. To tackle this challenge, we propose Triplet Detection (TD), a triplet learning-based vulnerabilities detection framework, to preserve invariant vulnerability knowledge across multiple Solidity versions. In TD, we propose a novel Smart Offline Mining (SOM) strategy to guide triplet selection, ensuring the learned embedding capture both foundational and version-independent vulnerability features. The experimental results demonstrate that TD outperforms state-of-the-art vulnerability detection tools and baseline methods. Furthermore, TD achieves superior performance in detecting vulnerabilities across different versions of smart contracts (e.g., from v0.4 to v0.8), highlighting its capability to tackle the challenges of long-term reliability and adaptability in detection models due to the updates of smart contract versions.
We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smart contracts. LISA leverages data from historical audit reports to learn the detection experience (without model fine-tuning), enabling it to generalize learned patterns to unseen projects and evolving threat profiles. In our evaluation, LISA significantly outperforms both LLM-based approaches and traditional static analysis tools, achieving superior coverage of vulnerability types and higher detection accuracy. Our results suggest that LISA offers a compelling solution for industry: delivering more reliable and comprehensive vulnerability detection while reducing the dependence on manual effort.
Code reuse is a common practice in software engineering. Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract. Indeed, prior empirical studies have identified a number of issues caused by code reuse in smart contracts. Unfortunately, automatically detecting such issues poses several unique challenges. Particularly, in most cases, smart contracts are compiled as bytecode, whose class-level information (e.g., inheritance, virtual function table), and even semantics (e.g., control flow and data flow) are fully obscured as a single smart contract after compilation. Therefore, it is rather difficult to identify the reused parts of subcontract from a given smart contract, not to mention finding potential vulnerabilities caused by subcontract misuse.In this paper, we propose Satellite, a new bytecode-level static analysis framework for subcontract misuse vulnerability (SMV) detection in smart contracts. Satellite incorporates a series of novel designs to enhance its overall effectiveness.. Particularly, Satellite utilizes a transfer learning method to recover the inherited methods, which are critical for identifying subcontract reuse in smart contracts. Further, Satellite extracts a set of fine-grained method-level features and performs a method-level comparison, for identifying the reuse part of subcontract in smart contracts. Finally, Satellite summarizes a set of SMV indicators according to their types, and hence effectively identifies SMVs. To evaluate Satellite, we construct a dataset consisting of 58 SMVs derived from real-world attacks and collect additional 56 SMV patterns from SOTA studies. Experiment results indicate that Satellite exhibits good performance in identifying SMV, with a precision rate of 84.68% and a recall rate of 92.11%. In addition, Satellite successfully identifies 14 new/unknown SMV over 10,011 realworld smart contracts, affecting a total amount of digital assets worth 201,358 USD.
Віталій Носов, Oleksandr Manzhai, Svitlana Luchyk, Vasyl Luchyk
With the rapid advancement of smart contracts, ensuring their security has become critically important. A key aspect of this security-both for potential users and for law enforcement investigating offenses involving smart contracts (modus operandi)-is the ability to interpret the logic of published contracts and verify the consistency between deployed bytecode and the original source code. The authors' research demonstrates that such verification was successfully achieved only within the Remix integrated development environment (IDE) using the Contract Verification-Etherscan plugin. Upon activation, the plugin verified the consistency of the deployed bytecode with the contract's source code, which was subsequently published on Etherscan and marked as "Contract Source Code Verified (Exact Match)." Similar results were obtained during testing with a smart contract for issuing non-fungible ERC721 tokens (NFTs). The study also identified potential vulnerabilities involving NFTs linked to digital assets stored off-chain at a URI defined during token issuance. These risks can be mitigated by employing an additional smart contract to ensure that NFT transfers include the associated digital asset, with the asset hosted on IPFS. The analysis and testing tools presented herein provide law enforcement with the means to understand the modus operandi of offenses involving smart contracts, thereby aiding in criminal investigations.
Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi
Smart contract technologies are revolutionizing the landscape of global finance by enabling secure, automated, and transparent cross-border financial transactions. Traditional international transactions are often hindered by delays, high costs, and reliance on multiple intermediaries such as correspondent banks, clearinghouses, and regulatory bodies. These processes not only increase operational complexity but also expose transactions to risks of fraud, errors, and regulatory inefficiencies. Smart contracts, built on blockchain platforms, address these challenges by embedding contractual terms directly into self-executing code that autonomously enforces obligations once pre-defined conditions are met. This technological innovation eliminates the need for third-party verification, reduces transaction latency, and ensures that funds or assets are exchanged only when contractual conditions are satisfied, thereby enhancing both trust and efficiency in global markets. The application of smart contracts in cross-border financial transactions streamlines settlement processes by providing real-time execution and verification, reducing the risk of human error and dispute. Automated compliance mechanisms can be integrated into the contract logic, ensuring adherence to international trade and financial regulations while minimizing manual oversight. Moreover, transparency inherent in blockchain technology allows stakeholders including regulators, financial institutions, and clients to access immutable transaction records, strengthening accountability and trust. The interoperability of smart contract platforms with emerging technologies such as digital currencies and decentralized finance ecosystems further amplifies their potential to reshape international trade and investment flows. Strategically, smart contracts promote inclusivity in global markets by lowering transaction costs, expanding access for small and medium-sized enterprises, and accelerating settlement times, particularly in regions where traditional banking infrastructure is underdeveloped. However, challenges remain, including legal recognition across jurisdictions, standardization of protocols, and ensuring resilience against cyber threats. Addressing these issues through coordinated governance and international regulatory cooperation will be critical for mainstream adoption. In summary, smart contract technologies enable secure, automated, and transparent cross-border financial transactions, offering significant advances in efficiency, cost reduction, and trust-building across global economic markets. Keywords: Smart Contracts, Blockchain, Cross-Border Transactions, Financial Automation, Global Economic Markets, Transparency, Compliance, Decentralized Finance.
Smart contracts are a key part of blockchain applications, and attackers can exploit them to manipulate contract behaviour or steal assets. Smart contracts often contain security vulnerabilities, either accidentally introduced by developers or due to flawed business logic. In this paper, we focus on finding an optimal Machine Learning based framework for detecting vulnerable smart contracts by analysing the smart contracts as embedding vectors. CodeBERT, a pre-trained transformer model, is used for feature extraction in the proposed framework. The framework has shown approximately 97% accuracy in detecting smart contracts that contain various vulnerabilities. Additionally, the research explores the performance of CodeBERT variants for this task. The results of the experiments have proven the favourability of this framework in detecting vulnerable smart contracts.
This chapter explores the intersection between the deterministic execution of smart contracts and the unpredictable nature of delay, a legal phenomenon historically embedded in human discretion and normative flexibility. While smart contracts promise automated, trustless enforcement, they reveal critical vulnerabilities when confronted with unforeseen disruptions, particularly in the context of technical rigidity and legislative gaps. The discussion navigates through the architectural challenges of code literalism, the oracle dependency problem, and the doctrinal limitations of classical contract law in adjudicating delays devoid of intent or culpability. It also examines emerging hybrid legal-technical frameworks, including regulatory innovations in the EU and UK, and the conceptual development of Lex Cryptographica. Ultimately, the chapter proposes a recalibration of contract theory and practice, advocating for a pluralistic approach that integrates technical resilience with normative safeguards to manage delay in a digitally autonomous age.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
Rad analizira digitalnu transformaciju u industriji osiguranja s posebnim naglaskom na primjenu blockchain tehnologije i pametnih ugovora. Istražuje kako telemetrija i oracle tehnologija omogućuju prikupljanje i korištenje podataka iz stvarnog svijeta za dinamično oblikovanje ugovora o osiguranju, što vodi razvoju novih modela poput mikroosiguranja, peer-to-peer osiguranja i osiguranja temeljenog na stvarnoj uporabi. Rad također razmatra pravne aspekte pametnih ugovora, njihovu pravnu valjanost, ograničenja u interpretaciji, te izazove u zaštiti privatnosti i regulatorne izazove koje donosi njihova primjena unutar EU i Republike Hrvatske. Poseban naglasak stavlja se na važnost stvaranja jasnih i prilagodljivih pravnih rješenja koja će omogućiti odgovornu i učinkovitu integraciju novih tehnologija u osigurateljnu praksu.
This study aims to provide a comprehensive analysis of tools and methods for ensuring smart contract security.The research employs a systematic review of static analysis, dynamic testing, and formal verification approaches.Static analysis tools, including Oyente, Mythril, and Slither, are systematically evaluated regarding their effectiveness in identifying vulnerabilities at early development stages, highlighting strengths in detecting known vulnerability patterns as well as limitations such as false positives.Dynamic analysis methodologies, such as fuzz testing (e.g., Echidna, Harvey) and symbolic execution (e.g., MAIAN, teEther), are assessed for their capability to identify complex logical vulnerabilities that are typically missed by static methods, examining their accuracy, scalability, and real-world applicability.Formal verification approaches employing K-framework, Why3, and Coq are thoroughly examined for their ability to deliver rigorous mathematical guarantees of smart contract correctness, along with their practical applicability, complexity, and integration into typical smart contract development workflows.The study reveals that an integrated security strategy, combining static analysis, dynamic testing, and formal verification methods, is essential for comprehensive and robust smart contract protection, effectively mitigating diverse vulnerabilities across the entire contract lifecycle.The research contributes to the field by offering a comparative analysis of current tools, identifying their strengths and limitations, and proposing future research directions, including automated specification generation and AI-driven vulnerability prediction.