Power industry software, as a core tool for modern power equipment control and management, is facing increasingly severe cybersecurity threats.Distributed ledger technology provides new ideas for power software security detection due to its decentralization, transparency and tamper-proof characteristics.This paper discusses the application of distributed ledger technology in the security detection of software development in the electric power industry, and proposes a trusted traceability and quality access control reinforcement method based on distributed ledger.The research designs the traceability data model and smart contract system to realize the trusted collection, storage and verification of security data; at the same time, it proposes the sensitive data aggregation method based on homomorphic encryption and the tamper-proof technology of RSA asymmetric encryption, and constructs the data communication structure of Overlay structure, which guarantees the complete transmission of electric power software security detection data and traceability tracking.The experimental results show that compared with SHA256 algorithm and DyRH model, the average value of the error localization time of this method is reduced to 9.23ms, which is 8.6ms and 4.1ms less than the control group, respectively; the accuracy rate of the error localization reaches 98.33%, which is improved by 4.77% and 1.79%; and in the test of the anti-attack performance, the average number of tampered data is only 189, which is respectively reduced by 184 and 156.The study proves that distributed ledger technology can effectively enhance data credibility, strengthen traceability, and enhance the strength of system quality access control in software development security detection in the power industry, which provides a new technical path and solution for the information security of the power system.
The robustness and reliability of blockchain applications, critically depend on thorough testing. This study introduces AGTS, an automated framework for generating smart contract test suites on Hyperledger Fabric, significantly reducing manual effort while improving test coverage and security. The framework integrates static and dynamic analysis, leveraging symbolic execution and fuzz testing to detect vulnerabilities. The project is implemented in C#, and uses three test cases: CoCoME, LibraryMS and LoanPS, with their requirements as input, and generates their closely related test cases. We generated 34 test cases for CoCoME, 32 for LibraryMS, and 28 for LoanPS. Combining the test cases, their executable scripts were generated. Finally, we tested all the targeted smart contracts rigorously against the predefined criteria. The generated scripts undergo the quality assurance using ShellCheck, deploying on the continuous integration system, that further enhances the reliability and maintainability of these tests. AGTS automates the entire process of test script creation and verification, drastically reducing the manual effort involved in preparing test cases. It facilitates rapid and thorough testing cycles by generating dedicated scripts that execute specific test scenarios on smart contracts, verifying their behavior and performance. By automating the tedious and error-prone task of writing test cases, AGTS accelerates the development process and fortifies the security framework of blockchain applications. Comprehensive evaluation of AGTS across diverse real world smart contracts demonstrated the effectiveness in defect detection, significantly outperforming existing methods. The contribution of AGTS extends beyond providing a practical testing tool, by offering developers a robust framework for ensuring the reliability and security of their smart contracts. AGTS not only expedites test preparation but also ensures comprehensive test coverage, thus improving the overall quality of blockchain applications.
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of July, 2025). We integrate multi-dimensional analyses including the automated parsing of Solidity interfaces, graph-based modeling of inheritance structures, contributor profiling, and mining of community discussion data. We distinguish foundational from emerging standards, expose poor cross-version interoperability, and show that growing functional complexity heightens security risks.
Ramazan BektaĆ, Kerim Eser AFĆAR, Ahmet Aydın Arı
Cryptocurrencies initially gained prominence by eliminating intermediaries in payment systems and later found applications in various business sectors. The crypto network, pioneered by Bitcoin, has spurred new business forms and organizational structures with diverse motivations. Bitcoin's emergence is technically dated to 2008. However, its ideological and technical roots trace back to the cyberpunk literature of the late 1970s and the cypherpunk movement that began in California in 1992. The cypherpunk manifestos significantly influenced cryptographic work, shaping Bitcoin's technical foundation. This study aims to explore Bitcoin's ideological origins through a qualitative content analysis of cypherpunk manifestos, Nakamoto's posts on the "Bitcointalk" forum, and "Cryptography Mailing List" correspondence. By examining these sources, the study identifies the historical dimensions of Bitcoin's technical structure and highlights the impact of ideological debates on its development. Findings reveal that while cryptographic research influenced Bitcoin's technical evolution, ideological discussions were relatively less significant. Nonetheless, Bitcoin's developers, particularly Nakamoto, incorporated a strong ideological emphasis on "privacy" despite the primary technical focus.
ABSTRACT Smart Contracts ensure trust through blockchain technology, streamline processes, and have disruptive potential across various industries. However, the issue of smart contract security cannot be underestimated. The vulnerability of smart contracts to exploitation has led to substantial losses, prompting increased attention toward vulnerability mining. Existing efforts for analyzing contract security heavily depend on inflexible rules set by experts, making them nonâadaptable or scalable. Although various machineâlearning methods have emerged for vulnerability mining in smart contracts, a research gap remains in effectively integrating diverse features of complex smart contracts with deep neural networks for enhanced detection. This paper presents SmartSecure, a vulnerability mining framework incorporating highâlevel semantic features extracted from contract source code. It provides inâdepth local insights into vulnerabilities through contract property graphs that integrate abstract syntax trees, control flow graphs, and data dependency graphs, encompassing all syntactic and semantic aspects of the contract function. To fortify these features, we integrate them with lowâlevel features derived from opcode sequences, encompassing global aspects. These diverse features are seamlessly fused and processed through a novel neural network design, resulting in a robust and effective solution. We evaluate our framework over 25,129 realâworld smart contracts. Extensive experiments demonstrate the superiority of our method over existing tools and neural networkâbased approaches. It achieves an exceptional performance level of up to 97.6%, marking a significant step forward in smart contract security.
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 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.
Merkle proof-based airdrops are a widely used mechanism in decentralized finance (DeFi) for distributing tokens securely and efficiently to eligible recipients. By leveraging Merkle trees, these airdrops minimize storage requirements and reduce transaction costs while ensuring the integrity of recipient lists. Unfortunately, their importance also attracts sophisticated attacks. Secondary preimage attacks, in particular, pose a major risk to the security of token distribution, potentially enabling adversaries to exploit vulnerabilities and drain tokens from airdrop contracts. This paper introduces FSA: a F ramework for analyzing S econdary preimage A ttacks on Merkle proof-based airdrops, designed to systematically address such vulnerabilities. The framework operates through four core contributions: (1) a validated quantitative damage assessment model using proof-of-concept experiments, (2) an analysis of attack repetition patterns in binary Merkle trees and Merkle Mountain Ranges, (3) identification of key factors influencing attack success, including token balances, hash values, and variable sizes, and (4) evaluation of mitigation strategies such as using non-64-byte leaf values, asymmetric hashing, and proof length validation. Through extensive analysis using the FSA, our experimental results demonstrate the framework's effectiveness in uncovering vulnerability patterns and developing targeted countermeasures.
In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems.
Smart contracts, while foundational to decentralized applications, are susceptible to security vulnerabilities due to their immutable nature, potentially leading to significant financial losses. Existing$s$tatic a nalysis$t$ools,$s$uch a slither and Mythril, offer baseline detection but often lack accuracy and scalability for complex contracts. Similarly, emerging deep learning methods show promise but face challenges, including oversimplified multi-class c lassifications, di fficulties processing long code sequences, and the constraint of assigning each contract to a single vulnerability category. To overcome these limitations, we propose a binary classification f ramework focused on determining whether a contract is secure or possesses at least one known vulnerability. This approach uniquely combines static heuristic features (e.g., control-flow complexity and external call frequency) with contextual semantic embeddings derived from CodeBERT. CodeBERT, a transformerbased model pre-trained on source code, provides rich semantic and syntactic representations that complement static features and enhance detection performance. Evaluating five distinct machine learning models on the SolidiFI and SmartBugs benchmark datasets, we demonstrate that this hybrid strategy significantly e nhances d etection p erformance. N otably, our Logistic XGBoost classifier a chieves$1 0 0 \%$a ccuracy, precision, and recall on SolidiFI, although we acknowledge that SolidiFI's relative simplicity may contribute to overly optimistic results and potential overfitting risks. On SmartBugs, ensemble models consistently achieve over 95% accuracy, indicating strong generalization across more diverse and complex contracts.
Aditya Shewale, Dhanyasree Thallapalli, S. Udhayakumar
Smart contracts, implemented on blockchain net-works, provide secure and decentralized transactions. Despite this, issues with Solidity-based smart contracts have resulted in major financial losses due to exploits like reentrancy attacks, integer overflows, and unauthorized access. In this paper, a thorough framework of vulnerability assessment that combines static, dynamic, and forensic analysis is proposed for the improvement of the security of smart contracts. Static analysis, facilitated by Slither, detects code-level vulnerabilities, whereas dynamic analysis, driven by Mythril, emulates attack patterns to find execution-time errors. Furthermore, a forensic analysis module utilizes transaction tracing and opcode analysis to study historical exploits and identify unusual contract behaviors. The proposed framework improves the accuracy of vulnerability detection, reduces false positives, and strengthens the security of smart contracts by offering security insights. Empirical assessment illustrates the efficiency of the framework in detecting security vulnerabilities with a high detection rate and low overhead. This work adds to reinforcing blockchain security by providing an automated, multi-layered method of auditing smart contracts
Kassim Kalinaki, Owais Ahmed Malik, Gusti Ahmad Fanshuri Alfarisy, Jalia Nassanga
The fusion of blockchain technologies and non-fungible tokens (NFTs) into smart city ecosystems presents new security challenges, impeding the widespread adoption of NFT in urban settings. Accordingly, this study comprehensively reviews the cybersecurity aspects surrounding NFTs within smart city environments. Firstly, a discussion of the various applications of NFTs in smart cities is provided. This is followed by an exploration of the unique cybersecurity vulnerabilities emanating from implementing NFTs in smart city ecosystems, including data privacy issues, smart contract vulnerabilities, token theft, and the potential for market manipulation, etc. Moreover, various countermeasures and best practices to negate NFTs' cybersecurity concerns and vulnerabilities have been detailed. Finally, emerging trends in NFT security are equally also analyzed. This review study provide urban planners, policymakers, technologists, students, and researchers with a refined understanding of the cybersecurity concerns of NFT in smart cities.
With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.
Smart contract vulnerabilities have led to billions in losses, yet finding actionable exploits remains challenging. Traditional fuzzers rely on rigid heuristics and struggle with complex attacks, while human auditors are thorough but slow and don't scale. Large Language Models offer a promising middle ground, combining human-like reasoning with machine speed. Early studies show that simply prompting LLMs generates unverified vulnerability speculations with high false positive rates. To address this, we present A1, an agentic system that transforms any LLM into an end-to-end exploit generator. A1 provides agents with six domain-specific tools for autonomous vulnerability discovery, from understanding contract behavior to testing strategies on real blockchain states. All outputs are concretely validated through execution, ensuring only profitable proof-of-concept exploits are reported. We evaluate A1 across 36 real-world vulnerable contracts on Ethereum and Binance Smart Chain. A1 achieves a 63% success rate on the VERITE benchmark. Across all successful cases, A1 extracts up to \$8.59 million per exploit and \$9.33 million total. Using Monte Carlo analysis of historical attacks, we demonstrate that immediate vulnerability detection yields 86-89% success probability, dropping to 6-21% with week-long delays. Our economic analysis reveals a troubling asymmetry: attackers achieve profitability at \$6,000 exploit values while defenders require \$60,000 -- raising fundamental questions about whether AI agents inevitably favor exploitation over defense.
The escalating complexity and frequency of malware attacks pose a significant challenge to conventional cybersecurity frameworks, particularly in scenarios demanding high data privacy and cross-organizational threat intelligence sharing. Traditional centralized machine learning models for malware detection often rely on aggregating data in a central server, thereby increasing the risk of data breaches and limiting the deployment of models in privacy-sensitive environments such as healthcare, finance, and critical infrastructure. To address these limitations, this study explores an integrated approach that combines Federated Learning (FL) with Explainable Artificial Intelligence (XAI) for enhancing malware detection while preserving user privacy and system confidentiality. Federated learning enables the collaborative training of robust malware classifiers across multiple decentralized nodes without sharing raw data, thus maintaining local data sovereignty and complying with data protection regulations. The proposed framework incorporates deep learning architectures such as convolutional neural networks (CNNs) trained in a federated environment using feature vectors extracted from malicious binaries and behavior logs. To ensure transparency and trust in model predictions, explainable AI techniques specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated, providing actionable insights into the modelâs decision-making process. This study also presents a comprehensive evaluation using a benchmark malware dataset distributed across simulated client environments, measuring detection accuracy, communication overhead, privacy leakage, and interpretability performance. Results demonstrate that the FL-XAI approach achieves detection rates comparable to centralized models while ensuring data confidentiality and interpretability. The research contributes to the evolving field of privacy-preserving threat intelligence by offering a scalable and explainable framework suitable for real-time cybersecurity applications.
K. Praveen Kumar, Shaik Lubna, Pullagurla Tharun Kumar
Smart contracts are an integral part of blockchain networks, enabling automation and security of transactions. But undetected smart contract vulnerabilities can be exploited severely. Manual code review and static analysis, which are traditional methods for detecting vulnerabilities, tend to be time-consuming and do not possess the capability to identify sophisticated flaws. This work proposes a new method using a tailored Convolutional Neural Network (CNN) to examine Ethereum Solidity smart contract bytecode to provide effective and strong vulnerability detection. Our model is highly accurate and efficient, with 87.75 % accuracy, 86.38 % precision, 87.09 % recall, and an F1-score of 86.22 % on the Slither audited smart contract dataset. This innovation offers an efficient and scalable solution for Ethereum-based smart contracts security.
As the core application of blockchain technology, smart contracts have been widely used in many fields such as finance, supply chain, and copyright management. Smart contracts are prone to various vulnerabilities that attackers can exploit to steal or freeze funds. Traditional vulnerability detection methods rely heavily on complex rules defined by experts, which are difficult to adapt to the explosion of smart contracts. Some recent studies of neural network-based vulnerability detection methods rely on contract source code, and the accuracy of bytecode-level vulnerability detection methods is low. To overcome the limitations of existing methods, we propose CV-IRTL, a new method for smart contract vulnerability detection. Specifically, CV-IRTL designs a vulnerability detection framework for smart contracts based on inverted residual network architecture and transfer learning. In particular, CV-IRTL enables vulnerability detection at the bytecode level, simplifies data preprocessing, utilizes transfer learning to better capture vulnerability characteristics and effectively address dataset imbalances. We have extensively tested CV-IRTL on a dataset containing six vulnerabilities. The experimental results show that the macro average F1-score is 90.75%, and the overall false positive rate is 9.6%, which is better than representative methods in performance.
The widespread adoption of EOSIO blockchain technology has underscored the critical importance of securing EOSIO smart contracts. Vulnerabilities within these contracts can result in substantial economic losses, making their detection a vital area of research. However, existing methods for detecting vulnerabilities in EOSIO smart contracts predominantly rely on expert-defined rules, which are often susceptible to errors and lack scalability. To address this issue, we propose a vulnerability detection method for the EOSIO platform based on a teacher-student network architecture. This approach specifically focuses on detecting vulnerabilities at the function level. This approach consists of two networks. The teacher network learns both the syntax and semantics of source code and bytecode, while the student network takes bytecode as input. The teacher-student network extracts function-level features by incorporating an MCOAttention mechanism. The student network infers missing bytecode embeddings by learning from the teacher network. By combining the inferred source code and bytecode representations, the method achieves improved accuracy in vulnerability detection. We introduce a cross-modal mutual learning strategy to facilitate knowledge transfer between the teacher and student networks. We evaluated our proposed approach using a dataset comprising smart contracts from the EOSIO platform. Experimental results demonstrate that our method significantly improves accuracy in vulnerability detection.
The cybersecurity threats targeting industrial control systems (ICS) are evolving with increasing sophistication. Addressing the detection blind spots in existing source code analysis techniques, this study reveals a dual security paradox arising from code sensitivity: privacy leakage risks caused by decompilation techniques and integrity verification deficiencies in reverse engineering. This paper investigates three critical challenges: (1) What are the component flow process and detection elements of ICS component source code? (2) How can high-performance and reliable tracing and traceability be provided for ICS component source code exceptions and routine detection? (3) How can privacy enhancement and trusted detection of ICS component source code with high sensitivity be achieved? This paper proposes a blockchain-integrated trusted detection framework for ICS (BCTD-ICS), delivering groundbreaking solutions: (1) Establishing a lifecycle circulation model that systematically maps component types, stakeholders, and detection parameters; (2) Developing a tripartite collaborative architecture (Blockchain-Identification Resolution Zero-knowledge proofs (ZKPs)), featuring a traceability mechanism with trusted identification codes (resolution efficiency: 40ms/105 queries) to eliminate decompilation-induced privacy risks; (3) Creating an industrial-oriented privacy enhancement system utilizing DBSCAN clustering for intelligent sampling (26% compression rate on BCN3D Moveo) and optimizing ZK-SNARK protocols through Shamirâs Secret Sharing, establishing a backdoor-resistant distributed parameter generation system (time delay increment < 100ms). Experimentally verified, our solution enables ICS component code detection supply-chain-wise without sensitive data leakage in real-world industries. This work establishes a novel trusted detection paradigm for ICS, advancing detection efficiency and credibility under strict privacy preservation requirements, meeting Industry 4.0 security demands.
Smart Grid Security and Resilience
Physical Unclonable Functions (PUFs) and Hardware Security
The chapter aims to illuminate the digital forensics of cryptocurrencies and the dark web by reviewing the role of the elements and tools involved in their formation, such as blockchain, computers, and mobile phones, and learning evidence. It will focus on its methods and review the extent to which artificial intelligence and machine language can be relied upon in forensics on the dark web. The chapter identified the need to address several areas of digital cryptocurrency forensics, in which gaps can be filled by developing advanced solutions for cryptocurrency forensics. Further investigation is required in digital forensics concerning significant cryptocurrencies like Monero, Ethereum, Verge, Dogecoin, and others. This is necessary because these currencies are becoming increasingly popular among both legitimate users and evil individuals. The survey highlighted another research gap: the limited amount of substantial research on host-based cryptocurrency forensics, particularly in mobile-based cryptocurrency forensics.
Xing Su, Hanzhong Liang, Hao Wu, Ben Niu · 6 authors
Understanding the Ethereum smart contract bytecode is essential for ensuring cryptoeconomics security. However, existing decompilers primarily convert bytecode into pseudocode, which is not easily comprehensible for general users, potentially leading to misunderstanding of contract behavior and increased vulnerability to scams or exploits. In this paper, we propose DiSCo, the first LLMs-based EVM decompilation pipeline, which aims to enable LLMs to understand the opaque bytecode and lift it into smart contract code. DiSCo introduces three core technologies. First, a logic-invariant intermediate representation is proposed to reproject the low-level bytecode into high-level abstracted units. The second technique involves semantic enhancement based on a novel type-aware graph model to infer stripped variables during compilation, enhancing the lifting effect. The third technology is a flexible method incorporating code specifications to construct LLM-comprehensible prompts for source code generation. Extensive experiments illustrate that our generated code guarantees a high compilability rate at 75%, with differential fuzzing pass rate averaging at 50%. Manual validation results further indicate that the generated solidity contracts significantly outperforms baseline methods in tasks such as code comprehension and attack reproduction.