Dhanush K, Tamilvelan S, Hari L, R. Roopa Chandrika
The growing adoption of lightweight, scalable, and resistant to tampering security mechanisms in the face of broad use of Internet-of-Things (IoT) and edge computing devices requires such mechanisms to be not based on centralized trust or bulky cryptography. The promising answer to this is the concept of Physical Unclonable Functions (PUFs) which relies on naturally existing manufacturing differences to produce device-specific, unclonable responses. Nevertheless, existing PUF-based authentication systems have significant flaws, such as centralized Challenge-Response Pair (CRP) storage vulnerable to attacks, vulnerability to machine learning, and no support of secure data recovery and sharing in distributed settings. This paper suggests a distributed authentication and recoverable data sharing framework that reduces these drawbacks, PUF-as-a-Service (PUFaaS). PUFaaS presents a multi-dimensional space of CRP, spreading the domain of challenges with respect to various operational parameters to maximize security against modeling attacks. Helper data of fuzzy extractors are secured with Shamir's secret sharing over distributed nodes and without having single points of weakness. A mechanism based on fuzzy vaults provides recoverable data binding, whereby encryption keys or sensitive data can be re-assembled successfully on successful verification of stable PUF responses. Authentication is carried out by way of lightweight commitment protocol, message authentication protocol and optional zero-knowledge proof guarantees privacy. Experimental analysis shows that PUFaaS can be evaluated as having low false acceptance and rejection, high modeling attack resistance, and scale efficiently (appropriate to large-scale IoT and cloud-edge). PUFaaS will offer an effective, privacy-resilient, and scalable solution to distributed authentication and secure information exchange in a non-trusted environment by converting PUFs into a service-oriented architecture.
Physical Unclonable Functions (PUFs) and Hardware Security
Large Language Models (LLMs) have demonstrated significant potential in smart contract auditing. However, they are still susceptible to hallucinations and limited context-aware reasoning. In this article, we propose SmartAuditFlow, a dynamic Plan-Execute framework that customizes audit strategies based on the unique characteristics of each smart contract. Unlike static, rule-based workflows, our approach iteratively generates and refines audit plans in response to intermediate outputs and newly detected vulnerabilities. To improve reliability, the framework incorporates structured reasoning, prompt optimization, and external tools such as static analyzers and Retrieval-Augmented Generation (RAG). This multi-layered design reduces false positives and enhances the accuracy of vulnerability detection. Experimental results show that SmartAuditFlow achieves 100% accuracy on common vulnerability benchmarks and successfully identifies 13 additional CVEs missed by existing methods. These findings underscore the framework’s adaptability, precision, and practical utility as a robust solution for automated smart contract security auditing. The source code is available at: https://github.com/JimmyLin-afk/SmartAuditFlow .
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinctive features of web3 scams. Moreover, the current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. To overcome these challenges, we present ScamSweeper, a novel framework that emphasizes the dynamic evolution of transaction graphs, to identify web3 scams on Ethereum. ScamSweeper samples the network with a structure temporal random walk, which is an optimized sample walking method that considers both temporal attributes and structural information. Then, the directed graph encoder generates the features of each subgraph during different temporal intervals, sorting as a sequence. Moreover, a variational Transformer is utilized to extract the dynamic evolution in the subgraph sequence. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts, which are from the first 18 million block heights on Ethereum. Subsequently, we comprehensively analyze the distinctions in various attributes, including nodes, edges, and degree distribution. Our experiments indicate that ScamSweeper outperforms SIEGE, Ethident, and PDTGA in detecting web3 scams, achieving a weighted F1-score improvement of at least 17.29% with the base value of 0.59. In addition, ScamSweeper in phishing node detection achieves at least a 17.5% improvement over DGTSG and BERT4ETH in F1-score from 0.80.
Xiangke Zhang, Chunxiao Ye, Ning Wang, Jelly Gan · 5 authors
With the growth of Ethereum, smart contracts have become a core component of blockchain platforms. However, the widespread adoption of smart contracts raises security concerns, as their immutability and interaction with digital assets make them attractive targets for attackers. Existing detection methods often struggle to address emerging, complex attack vectors and to accurately identify specific vulnerability types. We present SimSecLLM, an auditing framework grounded in code similarity that anchors LLM reasoning to vetted secure code snippets. The framework first builds a reference corpus containing more than 35,000 distinct security-vetted functions collected from 150 widely used libraries. For each smart contract under audit, it analyzes internal functions and their call relationships, establishes a detection order via topological sorting, and incorporates callee context. It then retrieves the closest secure exemplar from the curated reference corpus, extracts code-level modifications through comparison, and leverages a fine-tuned LLM in the detection stage to reason over these changes in order to assess whether they introduce vulnerabilities and to identify their potential types. SimSecLLM was evaluated on the DAppSCAN-source dataset with 3,527 function-level samples across seven types of vulnerabilities, demonstrating superior performance over existing methods with an accuracy of 93.1 % and an F1score of 87.1 %. A case study on the Redacted Cartel vulnerability illustrates that similarity-guided analysis effectively reveals security-critical code modifications, thereby supporting the identification of vulnerability root causes.
Love Allen Chijioke Ahakonye, Hamza Ibrahim, Jae-Min Lee, Dong‐Seong Kim
Smart contract environments are increasingly targeted by stealthy, adaptive attacks that evade conventional rule-based or static anomaly detection systems. Inspired by the anglerfish’s bioluminescent filament, which perceives and lures activity in dark, dynamic environments, this research introduces a Bioluminescent Filament-Inspired Artificial Intelligence Perception framework for smart contract intrusion detection. The proposed model emulates biological sensory adaptation through multi-modal attention layers that dynamically illuminate anomalous behaviors in contract execution flows. By integrating self-supervised temporal perception with context-driven feedback, the framework continuously refines its detection sensitivity while maintaining low computational overhead. We evaluate the framework using fuzz-tested smart contract vulnerability datasets that simulate diverse malicious execution behaviors observed in Ethereum environments, demonstrating over 98% detection accuracy with a 40% reduction in latency compared to traditional deep learning-based IDS models. This biologically inspired perception paradigm offers a scalable, energy-efficient solution for securing blockchain-based decentralized systems against evolving threat vectors.
Uisang Lee, Changhoon Chung, Junmo Lee, Sung Jun Moon
The rapid growth of Ethereum has made it more important to quickly and accurately detect smart contract vulnerabilities. While machine-learning-based methods have shown some promise, many still rely on rule-based preprocessing designed by domain experts. Rule-based preprocessing methods often discard crucial context from the source code, potentially causing certain vulnerabilities to be overlooked and limiting adaptability to newly emerging threats. We introduce BugSweeper, an end-to-end deep learning framework that detects vulnerabilities directly from the source code without manual engineering. BugSweeper represents each Solidity function as a Function-Level Abstract Syntax Graph (FLAG), a novel graph that combines its Abstract Syntax Tree (AST) with enriched control-flow and data-flow semantics. Then, our two-stage Graph Neural Network (GNN) analyzes these graphs. The first-stage GNN filters noise from the syntax graphs, while the second-stage GNN conducts high-level reasoning to detect diverse vulnerabilities. Extensive experiments on real-world contracts show that BugSweeper significantly outperforms all state-of-the-art detection methods. By removing the need for handcrafted rules, our approach offers a robust, automated, and scalable solution for securing smart contracts without any dependence on security experts.
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.
Hackers may create malicious solidity programs and deploy it in the Ethereum block chain. These malicious smart contracts try to attack legitimate programs by exploiting its vulnerabilities such as reentrancy, tx.origin attack, bad randomness, deligatecall and so on. This may lead to drain of the funds, denial of service and so on . Hence, it is necessary to identify and prevent the malicious smart contract before deploying it into the blockchain. In this paper, we propose an ML based malicious smart contract detection mechanism by analyzing the EVM opcodes. After balancing the opcode frequency dataset with SMOTE algorithm, we transformed opcode frequencies to the binary values (0,1) using an entropy based supervised binning method. Then, an explainable AI model is trained with the proposed binary opcode based features. From the implementations, we found that the proposed mechanism can detect 99% of malicious smart contracts with a false positive rate of only 0.01. Finally, we incorporated LIME algorithm in our classifier to justify its predictions. We found that, LIME algorithm can explain why a particular smart contract app is declared as malicious by our ML classifier based on the binary value of EVM opcodes.
Smart contracts operating on blockchain platforms like Ethereum often face critical security challenges, which can lead to significant financial damage. Existing detection techniques frequently fall short in terms of precision or scalability. To overcome these limitations, we introduce an innovative deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks and incorporates an attention mechanism. This model is designed to extract both short-range syntactic cues and long-distance contextual patterns from smart contract code. By applying advanced preprocessing and employing the SMOTE algorithm to handle class imbalance, our approach excels in identifying key vulnerabilities, including Reentrancy, Timestamp Dependency, Integer Overflows, and Dangerous Delegatecall . Experimental evaluations reveal notable gains in detecting rare vulnerability types, particularly in terms of recall and precision, underscoring the effectiveness of attention-augmented hybrid models for enhancing smart contract security analysis.
The exponential growth of sophisticated cyber threats in Internet of Things (IoT) environments has exposed fundamental weaknesses in existing Cyber Threat Intelligence (CTI) platforms, including centralized architectures, trust deficits, privacy vulnerabilities, and single points of failure. To overcome these limitations, this paper proposes BlockIntelChain, a blockchain-based framework for secure, scalable, and collaborative CTI sharing across distributed IoT networks. The system integrates a hybrid consensus mechanism that combines Proof-of-Stake with reputation-based validator selection, supported by a multi-layered privacy framework employing Differential Privacy (DP), Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Secure Multi-Party Computation. BlockIntelChain further embeds Federated Learning (FL) to enable distributed model training directly on IoT edge nodes without exposing raw threat telemetry. Comprehensive evaluations on real-world Malware Information Sharing Platform (MISP) datasets show that BlockIntelChain achieves 923 Transactions per Second at 500 nodes with 99.6% consensus success, while maintaining resilience against 51% and Byzantine attacks tolerating up to 33% malicious validators. Privacy analysis confirms an optimized utility-privacy trade-off, with DP (ε = 0.1) preserving 92% data utility and ZKP achieving 94% verification accuracy. The FL-based models outperform centralized baselines, reaching 96.4% accuracy for IoT malware classification, 94.7% for phishing detection, and 95.2% for network anomaly identification. Economic modeling validates sustainability through contributor growth (156 → 1,245 in 12 months) and improved contribution quality (0.73 → 0.92). The proposed framework directly benefits Security Operation Centers and edge-deployed IoT systems by enabling real-time threat intelligence exchange with strong security, privacy, and efficiency. Comparative benchmarking demonstrates BlockIntelChain's superiority over MISP, ThreatConnect, and IBM X-Force in decentralization, privacy, and cost efficiency, positioning it as a transformative solution for next-generation privacy-aware CTI ecosystems.
In recent years,the security issues of smart contracts have become increasingly prominent,and vulnerability detection has become a key challenge.In scenarios where source code is not publicly available,bytecode-based detection methods have attracted significant attention.However,existing deep learning methods typically rely solely on sequences or graph structures,which makes it difficult to fully capture vulnerability features.To address this,this paper proposes a smart contract bytecode vulnerability detection method based on heterogeneous graphs and instruction sequences,called RGCN-ResNet1D(Relational Graph Convolutional Network and ResNet-based 1D Convolutional Network).This method models bytecode as a heterogeneous graph and instruction sequence,using a Relational Graph Convolutional Network(RGCN) to extract structural features and a ResNet-based 1D Convolutional Network(ResNet1D) to extract sequential features,and then fuses the two types of features for vulnerability detection.A cross-entropy loss function is also designed,which dynamically adjusts the weight based on the number of misclassified samples,effectively alleviating the class imbalance problem in the training set.Experimental results show that RGCN-ResNet1D achieves F1 scores of 95.43%,90.67%,and 92.31% for detecting integer overflow,timestamp dependency,and self-destruct vulnerabilities,respectively,significantly outperforming the comparison methods.
A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
One of the most promising technology of blockchain still suffers from multiple attacks. Attack detection is handled by various technologies to enhance the security. Hackers moto is to steal the digital currency here the discussion is of the digital currency Ethereum which also has suffered from the famous attack Re-entrancy attack. Though the usage of smart contact is highly believed by users, making the function to be called again and again leads to this kind of attack. Re-entrancy attacks pose a persistent threat to Ethereum smart contracts, enabling malicious users to repeatedly call vulnerable functions before the state variables are correctly updated. While traditional approaches rely heavily on symbolic execution and SMT solvers for vulnerability detection, these methods suffer from scalability and precision limitations. A novel hybrid framework that integrates dynamic execution tracing with AI-driven classification models, such as Quantum Neural Networks (QNN), for the robust detection of re-entrancy attacks. The system leverages taint analysis and runtime monitoring to generate behavioral features, which are then used to train and evaluate machine learning models. Experiments conducted on benchmark datasets demonstrate superior accuracy and reduced false positives when compared to conventional static analysis techniques, showcasing the effectiveness of combining dynamic and AI-based strategies for smart contract security.
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
Blockchain smart contracts have been a groundbreaking technology yet are still prone to numerous security vulnerabilities that can lead to large financial and operational losses. In our prior work, we presented an in-depth methodology for data preprocessing and dataset preparation towards facilitating effective vulnerability detection in smart contracts. In this paper, building on that work, we proceed with our work by presenting a new hybrid deep learning architecture that integrates Graph Neural Networks (GNN) and CodeBERT in an efficient way to capture both structural and semantic code features. The hybrid model processes parallel representations of smart contracts: the GNN extracts graph-based control and data flow dependency relations, and CodeBERT makes use of pretrained contextual source code token embeddings. The two embeddings are concatenated and then fed into a shared classifier to predict the existence and types of vulnerabilities. We test our approach on a diverse collection of smart contracts and compare it with single-model baselines. Our hybrid model outperforms individual GNN and CodeBERT approaches with significant performance gains in precision, recall, and F1-score for different types of vulnerabilities. These findings confirm the effectiveness of our fusion approach and introduce the possibility of employing hybrid deep learning models in real-world smart contract security auditing.
Issues in error handling may have critical consequences in blockchain software, ranging from silent execution with invalid states to denial of services due to unexpected crashes. This paper discusses the pitfalls of errors handling within blockchain frameworks written in Go such as Hyperledger Fabric, Tendermint Core (including its derivatives, e.g. CometBFT, Ignite), and other frameworks (e.g. Cosmos SDK), as well as the Ethereum implementation. Then, it explores how a static analysis approach can be applied for the automatic detection of such of issues, allowing to fix buggy code before deployment, i.e., when the code becomes difficult to patch being blockchain a trustless, distributed, and decentralized environment. Finally, we evaluate our analysis implementation within GoLiSA on a set of existing smart contracts and blockchain applications, empirically demonstrating the feasibility of the proposed approach.
Mobile Web3 faces catastrophic retention (< 5%) yielding effective acquisition costs of \$500 - \$1,000 per retained user. Existing solutions force an impossible tradeoff: embedded wallets achieve moderate usability but suffer inherent click-jacking vulnerabilities; app wallets maintain security at the cost of 2 - 3% retention due to download friction and context-switching penalties. We present SecureSign, a PWA-based architecture that adapts desktop browser extension security to mobile via EIP-6963 provider sandboxing. SecureSign isolates dApp execution in iframes within a trusted parent application, achieving click-jacking immunity and transaction integrity while enabling native mobile capabilities (push notifications, home screen installation, zero context-switching). Our drop-in SDK requires no codebase changes for existing Web3 applications. Threat model analysis demonstrates immunity to click-jacking, overlay, and skimming attacks while maintaining wallet interoperability across dApps.
Smart contracts are commonly audited through static analysis to explore vulnerabilities. However, static approaches typically produce heterogeneous findings rather than reproducible, executable proof-of-concept (PoC) test cases, leading to costly and ad hoc manual validation. Large language models (LLMs) offer a promising way to translate audit reports into PoC test cases, but face three major challenges: noisy inputs, lack of execution grounding, and missing runtime oracles. We present SmartPoC, an end-to-end approach for validating reported vulnerabilities in audit reports by generating and executing PoC test cases with automated exploitability verification. SmartPoC first extracts a focused function-level slice from each report to reduce noise, centering on the key functions referenced in a finding and augmenting them with execution-relevant neighbors. To improve executability, we wrap LLM-based PoC synthesis in a generate-repair-execute loop, combining deterministic pre-execution sanitization with feedback-driven post-execution debugging. We further use differential verification as an oracle to confirm the exploitability of generated test cases. On the SmartBugs-Vul and FORGE-Vul benchmarks, SmartPoC achieves confirmation precision of 98.32% and 98.65%, with recall of 84.17% and 85.28%, respectively. On a recent Etherscan verified-source corpus, SmartPoC confirms 64 bugs from 545 audit findings at an average cost of $0.03.
Zhuo Chen, Gaoqiang Ji, He Yun, Lei Wu · 5 authors
Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently needed to identify similar contracts. Traditional binary similarity detection methods are typically based on instruction stream or control flow graph (CFG), which have limitations on EVM bytecode due to specific features like low-level EVM bytecode and heavily-reused basic blocks. Moreover, the highly-diverse Solidity Compiler (Solc) versions further complicate accurate similarity detection. Motivated by these challenges, we propose a novel EVM bytecode representation called Stable-Semantic Graph (SSG), which captures relationships between 'stable instructions' (special instructions identified by our study). Moreover, we implement a prototype, Esim, which embeds SSG into matrices for similarity detection using a heterogeneous graph neural network. Esim demonstrates high accuracy in SSG construction, achieving F1-scores of 100% for control flow and 95.16% for data flow, and its similarity detection performance reaches 96.3% AUC, surpassing traditional approaches. Our large-scale study, analyzing 2,675,573 smart contracts on six EVM-compatible chains over a one-year period, also demonstrates that Esim outperforms the SOTA tool Etherscan in vulnerability search.
Smart contract vulnerabilities continue to cause significant financial losses, despite the implementation of security measures such as manual audits and bug bounty platforms. A critical component often required by these security measures is the proof-of-concept (PoC) exploit, which validates vulnerability exploitability, assesses impact severity, and guides developers in fixes. Existing tools have explored automated PoC generation with techniques like symbolic execution, fuzzing, and program synthesis. However, these approaches frequently fail to generate PoCs for vulnerabilities exploited in real-world incidents, primarily due to their limitations in handling complex transaction dependencies, navigating vast on-chain state spaces, or requiring extensive manual specifications. Our migration-based approach extracts critical information from documented security incidents and applies it to generate PoCs for similar vulnerable code. This approach leverages proven exploit patterns rather than generating PoCs from scratch. This approach is motivated by two key observations: the prevalence of code reuse in smart contracts (up to 90% at the function level) and the increasing availability of documented PoCs for real-world incidents. Our approach operates in three phases: (1) abstracting essential components (i.e., environment properties, attack logic, and verification checks) from existing PoCs into templates, (2) given a new target contract, selecting suitable templates with adapted values through clone-detection and property-feasibility analysis, and (3) generating and validating PoCs in simulated environments. Our evaluation demonstrates effectiveness and efficiency across multiple scales. Our approach successfully generates valid PoCs for 62 out of 67 manually validated cases without false positives and completes analysis in 3.8 hours compared to 133.2 and 210.5 hours required by existing tools. Large-scale evaluation on 979,512 contracts identifies 256 vulnerable contracts across blockchain networks with 64 cross-chain cases, demonstrating real-world applicability.
Han Liu, Daoyuan Wu, Yi Sun, Shuai Wang · 6 authors
OpenZeppelin is a building block for many smart contracts on Ethereum-compatible blockchains. It provides mod-ular and reusable libraries for various Ethereum standards (e.g., ERC20 and ERC721) and common functionalities such as upgradeable contracts. Little research has been done on Open-Zeppelin security except for a recent study, which focused only on the misuse of OpenZeppelin code, assuming OpenZeppelin itself is secure but contract developers may not follow OpenZeppelin’s function checks appropriately. We argue that, despite appearing robust, OpenZeppelin itself could have many vulnerabilities, and these library-level vulnerabilities could inadvertently affect third-party smart contracts, even without misuse from developers.We present ZepCompare, the first end-to-end system for demystifying OpenZeppelin’s own vulnerabilities and analyzing their propagation in third-party smart contracts. ZepCompare incorporates a manual analysis stage where we review OpenZeppelin’s 64 historical releases, identifying 109 vulnerable-fixed code pairs, exposing flaws in cryptographic utilities, access control, etc. Leveraging these pairs, ZepCompare introduces facts of changes, a novel structure capturing vulnerable and fixed code contexts for flexible matching. Evaluated across 88,605 contracts from three Ethereum-compatible chains, ZepCompare detects 4,708 instances of OpenZeppelin-derived vulnerabilities. Manual sampling and a ground-truth experiment confirm that ZepCompare achieves 86.7% precision and 77.1% recall. Our findings reveal significant security risks in both historical and the latest versions of OpenZeppelin libraries, underscoring the urgent need for systematic auditing of foundational contracts components.
Solana has rapidly evolved into a leading next generation platform for supporting decentralized applications due to its high performance and low transaction costs. Its new contract execution model, which decouples code logic from states, gives rise to new vulnerability threats that can result in significant financial losses for users within the ecosystem. However, existing studies towards detecting vulnerabilities are predominantly tailored for Ethereum smart contracts, which are unsuitable for Solana platform because of the variations in implementation languages and runtime semantics. In this paper, we propose Soleker, a novel approach that leverages learning-based techniques to automatically identifying potential vulnerabilities in Solana smart contract bytecode. More specifically, Soleker captures runtime semantic information from instructions that are associated with blockchain interactions and extracts vulnerability-specific localized features. Then, a prefix-guided graph learning model is introduced to learn and integrate extracted features, enabling effective vulnerability detection. We conduct experiments on a newly constructed contract dataset and the results demonstrate that Soleker significantly outperforms the baseline methods, achieving an average effectiveness improvement of 126.4% and a 335× boost in efficiency.