arXiv subjects

Xiaoqi Li

Publications and source records attributed to Xiaoqi Li.

At least 73 records · Page 4Linked to original sources

arXiv2026

Security Analysis of Web Applications Based on Gruyere

With the rapid development of Internet technologies, web systems have become essential infrastructures for modern information exchange and business operations. However, alongside their expansion, numerous security vulnerabilities have emerged, making web security a critical research focus within the broader field of cybersecurity. These issues are closely related to data protection, privacy preservation, and business continuity, and systematic research on web security is crucial for mitigating malicious attacks and enhancing the reliability and robustness of network systems. This paper first reviews the OWASP Top 10, summarizing the types, causes, and impacts of common web vulnerabilities, and illustrates their exploitation mechanisms through representative cases. Building upon this, the Gruyere platform is adopted as an experimental subject for analyzing known vulnerabilities. The study presents detailed reproduction steps for specific vulnerabilities, proposes comprehensive remediation strategies, and further compares Gruyere's vulnerabilities with contemporary real-world cases. The findings suggest that, although Gruyere's vulnerabilities are relatively outdated, their underlying principles remain highly relevant for explaining a wide range of modern security flaws. Overall, this research demonstrates that web system security analysis based on Gruyere not only deepens the understanding of vulnerability mechanisms but also provides practical support for technological innovation and security defense.

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arXiv2026

System Password Security: Attack and Defense Mechanisms

System passwords serve as critical credentials for user authentication and access control when logging into operating systems or applications. Upon entering a valid password, users pass verification to access system resources and execute corresponding operations. In recent years, frequent password cracking attacks targeting system passwords have posed a severe threat to information system security. To address this challenge, in-depth research into password cracking attack methods and defensive technologies holds significant importance. This paper conducts systematic research on system password security, focusing on analyzing typical password cracking methods such as brute force attacks, dictionary attacks, and rainbow table attacks, while evaluating the effectiveness of existing defensive measures. The experimental section utilizes common cryptanalysis tools, such as John the Ripper and Hashcat, to simulate brute force and dictionary attacks. Five test datasets, each generated using Message Digest Algorithm 5 (MD5), Secure Hash Algorithm 256-bit (SHA 256), and bcrypt hash functions, are analyzed. By comparing the overall performance of different hash algorithms and password complexity strategies against these attacks, the effectiveness of defensive measures such as salting and slow hashing algorithms is validated. Building upon this foundation, this paper further evaluates widely adopted defense mechanisms, including account lockout policies, multi-factor authentication, and risk adaptive authentication. By integrating experimental data with recent research findings, it analyzes the strengths and limitations of each approach while proposing feasible improvement recommendations and optimization strategies.

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arXiv2026

Information Security Based on LLM Approaches: A Review

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.

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arXiv2026

SCALM: Detecting Bad Practices in Smart Contracts Through LLMs

As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they do elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 35 specific issues. Specifically, we propose a large language models (LLMs)-based framework, SCALM. It combines Step-Back Prompting and Retrieval-Augmented Generation (RAG) to identify and address various bad practices effectively. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.

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arXiv2026

SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode

Smart contracts deployed on blockchain platforms are vulnerable to various security vulnerabilities. However, only a small number of Ethereum contracts have released their source code, so vulnerability detection at the bytecode level is crucial. This paper introduces SmartBugBert, a novel approach that combines BERT-based deep learning with control flow graph (CFG) analysis to detect vulnerabilities directly from bytecode. Our method first decompiles smart contract bytecode into optimized opcode sequences, extracts semantic features using TF-IDF, constructs control flow graphs to capture execution logic, and isolates vulnerable CFG fragments for targeted analysis. By integrating both semantic and structural information through a fine-tuned BERT model and LightGBM classifier, our approach effectively identifies four critical vulnerability types: transaction-ordering, access control, self-destruct, and timestamp dependency vulnerabilities. Experimental evaluation on 6,157 Ethereum smart contracts demonstrates that SmartBugBert achieves 90.62% precision, 91.76% recall, and 91.19% F1-score, significantly outperforming existing detection methods. Ablation studies confirm that the combination of semantic features with CFG information substantially enhances detection performance. Furthermore, our approach maintains efficient detection speed (0.14 seconds per contract), making it practical for large-scale vulnerability assessment.

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arXiv2026

Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM

Decentralized applications (DApps) face significant security risks due to vulnerabilities in smart contracts, with traditional detection methods struggling to address emerging and machine-unauditable flaws. This paper proposes a novel approach leveraging fine-tuned Large Language Models (LLMs) to enhance smart contract vulnerability detection. We introduce a comprehensive dataset of 215 real-world DApp projects (4,998 contracts), including hard-to-detect logical errors like token price manipulation, addressing the limitations of existing simplified benchmarks. By fine-tuning LLMs (Llama3-8B and Qwen2-7B) with Full-Parameter Fine-Tuning (FFT) and Low-Rank Adaptation (LoRA), our method achieves superior performance, attaining an F1-score of 0.83 with FFT and data augmentation via Random Over Sampling (ROS). Comparative experiments demonstrate significant improvements over prompt-based LLMs and state-of-the-art tools. Notably, the approach excels in detecting non-machine-auditable vulnerabilities, achieving 0.97 precision and 0.68 recall for price manipulation flaws. The results underscore the effectiveness of domain-specific LLM fine-tuning and data augmentation in addressing real-world DApp security challenges, offering a robust solution for blockchain ecosystem protection.

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arXiv2026

Malicious Code Detection in Smart Contracts via Opcode Vectorization

With the booming development of blockchain technology, smart contracts have been widely used in finance, supply chain, Internet of things and other fields in recent years. However, the security problems of smart contracts become increasingly prominent. Security events caused by smart contracts occur frequently, and the existence of malicious codes may lead to the loss of user assets and system crash. In this paper, a simple study is carried out on malicious code detection of intelligent contracts based on machine learning. The main research work and achievements are as follows: Feature extraction and vectorization of smart contract are the first step to detect malicious code of smart contract by using machine learning method, and feature processing has an important impact on detection results. In this paper, an opcode vectorization method based on smart contract text is adopted. Based on considering the structural characteristics of contract opcodes, the opcodes are classified and simplified. Then, N-Gram (N=2) algorithm and TF-IDF algorithm are used to convert the simplified opcodes into vectors, and then put into the machine learning model for training. In contrast, N-Gram algorithm and TF-IDF algorithm are directly used to quantify opcodes and put into the machine learning model training. Judging which feature extraction method is better according to the training results. Finally, the classifier chain is applied to the intelligent contract malicious code detection.

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arXiv2026

When Blockchain Meets Crawlers: Real-time Market Analytics in Solana NFT Markets

In this paper, we design and implement a web crawler system based on the Solana blockchain for the automated collection and analysis of market data for popular non-fungible tokens (NFTs) on the chain. Firstly, the basic information and transaction data of popular NFTs on the Solana chain are collected using the Selenium tool. Secondly, the transaction records of the Magic Eden trading market are thoroughly analyzed by combining them with the Scrapy framework to examine the price fluctuations and market trends of NFTs. In terms of data analysis, this paper employs time series analysis to examine the dynamics of the NFT market and seeks to identify potential price patterns. In addition, the risk and return of different NFTs are evaluated using the mean-variance optimization model, taking into account their characteristics, such as illiquidity and market volatility, to provide investors with data-driven portfolio recommendations. The experimental results show that the combination of crawler technology and financial analytics can effectively analyze NFT data on the Solana blockchain and provide timely market insights and investment strategies. This study provides a reference for further exploration in the field of digital currencies.

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arXiv2026

AtomGraph: Tackling Atomicity Violation in Smart Contracts using Multimodal GCNs

Smart contracts are a core component of blockchain technology and are widely deployed across various scenarios. However, atomicity violations have become a potential security risk. Existing analysis tools often lack the precision required to detect these issues effectively. To address this challenge, we introduce AtomGraph, an automated framework designed for detecting atomicity violations. This framework leverages Graph Convolutional Networks (GCN) to identify atomicity violations through multimodal feature learning and fusion. Specifically, driven by a collaborative learning mechanism, the model simultaneously learns from two heterogeneous modalities: extracting structural topological features from the contract's Control Flow Graph (CFG) and uncovering deep semantics from its opcode sequence. We designed an adaptive weighted fusion mechanism to dynamically adjust the weights of features from each modality to achieve optimal feature fusion. Finally, GCN detects graph-level atomicity violation on the contract. Comprehensive experimental evaluations demonstrate that AtomGraph achieves 96.88% accuracy and 96.97% F1 score, outperforming existing tools. Furthermore, compared to the concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.

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arXiv2026

No More Hidden Pitfalls? Exposing Smart Contract Bad Practices with LLM-Powered Hybrid Analysis

As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 47 specific issues. Specifically, we propose SCALM, an LLM-powered framework featuring two methodological innovations: (1) A hybrid architecture that combines context-aware function-level slicing with knowledge-enhanced semantic reasoning via extensible vectorized pattern matching. (2) A multi-layer reasoning verification system connects low-level code patterns with high-level security principles through syntax, design patterns, and architecture analysis. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.

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arXiv2026

DoS Attacks and Defense Technologies in Blockchain Systems: A Hierarchical Analysis

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.

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arXiv2026

Decentralized COVID-19 Health System Leveraging Blockchain

With the development of the Internet, the amount of data generated by the medical industry each year has grown exponentially. The Electronic Health Record (EHR) manages the electronic data generated during the user's treatment process. Typically, an EHR data manager belongs to a medical institution. This traditional centralized data management model has many unreasonable or inconvenient aspects, such as difficulties in data sharing, and it is hard to verify the authenticity and integrity of the data. The decentralized, non-forgeable, data unalterable and traceable features of blockchain are in line with the application requirements of EHR. This paper takes the most common COVID-19 as the application scenario and designs a COVID-19 health system based on blockchain, which has extensive research and application value. Considering that the public and transparent nature of blockchain violates the privacy requirements of some health data, in the system design stage, from the perspective of practical application, the data is divided into public data and private data according to its characteristics. For private data, data encryption methods are adopted to ensure data privacy. The searchable encryption technology is combined with blockchain technology to achieve the retrieval function of encrypted data. Then, the proxy re-encryption technology is used to realize authorized access to data. In the system implementation part, based on the Hyperledger Fabric architecture, some functions of the system design are realized, including data upload, retrieval of the latest data and historical data. According to the environment provided by the development architecture, Go language chaincode (smart contract) is written to implement the relevant system functions.

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arXiv2026

USCSA: Evolution-Aware Security Analysis for Proxy-Based Upgradeable Smart Contracts

In the case of upgrading smart contracts on blockchain systems, it is essential to consider the continuity of upgrades and subsequent maintenance. In practice, upgrade operations often introduce new vulnerabilities. Existing static analysis tools usually only scan a single version and are unable to capture the correlation between code changes and emerging risks. To address this, we propose an Upgradeable Smart Contract Security Analyzer, USCSA, which uses Abstract Syntax Tree (AST) difference analysis to assess risks associated with the upgrade process and utilizes large language models (LLMs) for assisted reasoning to achieve high-confidence vulnerability attribution. We collected and analyzed 3,546 cases of vulnerabilities in upgradeable contracts, covering common vulnerability categories such as reentrancy, access control flaws, and integer overflow. Experimental results show that USCSA achieves a precision of 92.26%, a recall of 89.67%, and an F1-score of 90.95% in detecting upgrade-induced vulnerabilities. As a result, USCSA provides a significant advantage to improve the security and integrity of upgradeable smart contracts, offering a novel and efficient solution for security auditing on blockchain applications.

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arXiv2026

AI-Based Vulnerability Analysis of NFT Smart Contracts

With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.

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arXiv2026

Security Vulnerabilities in Ethereum Smart Contracts: A Systematic Analysis

Smart contracts are a secure and trustworthy application that plays a vital role in decentralized applications in various fields such as insurance,the internet, and gaming. However, in recent years, smart contract security breaches have occurred frequently, and due to their financial properties, they have caused huge economic losses, such as the most famous security incident "The DAO" which caused a loss of over $60 million in Ethereum. This has drawn a lot of attention from all sides. Writing a secure smart contract is now a critical issue. This paper focuses on Ether smart contracts and explains the main components of Ether, smart contract architecture and mechanism. The environment used in this paper is the Ethernet environment, using remix online compilation platform and Solidity language, according to the four security events of American Chain, The DAO, Parity and KotET, the principles of integer overflow attack, reentrant attack, access control attack and denial of service attack are studied and analyzed accordingly, and the scenarios of these vulnerabilities are reproduced, and the measures to prevent them are given. Finally, preventive measures are given. In addition, the principles of short address attack, early transaction attack and privileged function exposure attack are also introduced in detail, and security measures are proposed. As vulnerabilities continue to emerge, their classification will also evolve. The analysis and research of the current vulnerabilities are also to lay a solid foundation for avoiding more vulnerabilities.

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arXiv2026

UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis

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.

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arXiv2026

MultiCFV: Detecting Control Flow Vulnerabilities in Smart Contracts Leveraging Multimodal Deep Learning

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.

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arXiv2026

NATLM: Detecting Defects in NFT Smart Contracts Leveraging LLM

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.

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