Serving as the first touch point for users to the cryptocurrency world, cryptocurrency wallets allow users to manage, receive, and transmit digital assets on blockchains and interact with emerging decentralized finance (DeFi) applications. Unfortunately, cryptocurrency wallets have always been the prime targets for attackers, and incidents of wallet breaches have been reported from time to time. Although some recent studies have characterized the vulnerabilities and scams related to wallets, they have mostly been studied at a coarse granularity, overlooking potential risks inherent in detailed designs of cryptocurrency wallets, especially from perspectives including user interaction and advanced features. To fill the void, in this paper, we present a fine-grained security analysis of browser-based cryptocurrency wallets. To pinpoint security issues in wallet components, we design WalleTruth, a visual-oriented testing framework specifically for browser-based wallet extensions. We have identified 12 attack vectors that can be abused by attackers to exploit cryptocurrency wallets and exposed 21 concrete attack strategies. By applying WalleTruth on 39 widely-adopted browser-based wallet extensions, we find that all of them can be abused to steal crypto assets from innocent users. Identified potential attack vectors were reported to developers in a timely manner and 26 issues have been patched already. This calls for urgent action from the community to mitigate threats related to cryptocurrency wallets.
The rapid proliferation of Internet of Things (IoT) devices across smart homes, healthcare systems, and industrial environments has intensified the need for robust and adaptive security mechanisms in multi-user settings. Traditional password management approaches remain widely deployed; however, they suffer from persistent vulnerabilities including weak password selection, credential reuse across services, and the absence of structured lifecycle management mechanisms. This paper presents a systematic review of existing authentication, password management, and key lifecycle strategies applicable to multi-user IoT ecosystems. The study follows a structured review methodology to analyze and synthesize contemporary research contributions in the areas of context-aware authentication, secure key rotation, password expiry mechanisms, and lightweight cryptographic implementations. A comparative evaluation of diverse security techniques—such as one-time passwords (OTPs), zero-knowledge proofs (ZKP), symmetric and public-key cryptographic schemes, and machine learning-based threat detection models—is conducted with particular attention to device resource constraints, scalability challenges, and operational efficiency. Conceptual models, analytical tables, and comparative charts are utilized to highlight trade-offs between security strength, computational overhead, and system performance. The review identifies significant research gaps in integrating dynamic key rotation and expiry mechanisms into holistic, context-aware security architectures tailored for multi-user IoT environments. Finally, the paper outlines future research directions aimed at developing scalable, resource-efficient, and adaptive password lifecycle management frameworks for next-generation IoT systems. management frameworks for next-generation IoT systems.
Privacy is one of the fundamental rights of individuals in modern societies. Yet, the practical adoption of privacy-preserving technologies in daily interactions remains limited. Zero-knowledge proofs offer strong privacy guarantees but are often hindered by their technical complexity. In this paper, we advance the idea of verifiable QR codes that enable off-line verifiers to verify proofs encoded in QR codes. Based on this core idea, we build a novel QR-driven zkSNARK proof verification framework (i.e., zQR) for mobile platforms. The framework integrates blockchain for auditability, non-repudiation and logging; and large-language models for automatic circuit generation. We perform a security discussion of the framework by considering multiple attack surfaces. Furthermore, we present an experimental evaluation measuring temporal costs (proof generation and verification latency, QR code encoding and decoding latency) and financial costs (blockchain gas consumption). Our results demonstrate the feasibility of zQR as a proof-of-concept framework for privacy-preserving verification on mobile platform where proofs are compactly represented with QR code symbol version of 19 with low error correction level. Finally, we discuss potential applications, current limitations and future directions for the broader adoption of privacy-preserving technologies in daily interactions.
Ramya K, Anbu Karuppusamy Dr S, Ragunathan Dr Aravindhan
The internet has become integral to daily life, facilitating commerce, communication, and services; however, it also presents significant security vulnerabilities. I have been looking at 2025 online security, accumulating patterns both popular and non-popular until March. AI plays a critical role in identifying security threats in real time. However, it also empowers malicious actors to orchestrate more sophisticated attacks, it's also but it also empowers malicious actors to orchestrate sophisticated cyberattacks. Another major issue is Zero trust architecture, which aligns with decentralized and remote environments, it's all about not believing anyone until they prove it. Web3 comes next, a free-for-all paradise where decentralization seems great until you run across issues—hacks are plentiful. The worst things? ransomware that keeps individuals from using the internet, outdated injection methods, IoT trash that basically gives crooks access. People aren't just sitting there, though; cloud trickery and privacy breaches are fighting the war and keeping momentum. Still, it's a fight with absurd costs, inadequate help, and thieves always changing the goalposts. Remarkable, isn't it? Innovations such as prospective quantum shielding and self-repairing technologies intrigue me. I am presenting my findings regarding our current situation, the factors contributing to our failures, and potential solutions for overcoming these challenges—not a traditional lecture This paper presents a comprehensive synthesis of the author’s research and analysis aimed at enhancing internet resilience in 2025.
Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation. Existing automated exploit generation systems report binary pass/fail outcomes, discarding partial progress and producing no signal for the other two communities. This paper presents FORGE, a multi-agent system that bridges these three silos through graduated exploitation depth. Five specialized agents (Intel, Generator, Planner, Exploit, and Detector) execute in a fixed pipeline that (1) generates targeted vulnerable applications from CVE metadata, (2) conducts coached, multi-turn exploitation assessed by an LLM-primary oracle on a four-level taxonomy (L0: no evidence through L3: full compromise), and (3) produces Sigma and Snort detection rules grounded in OpenTelemetry exploitation traces. Graduated depth is the bridging mechanism: deeper exploitation yields richer behavioral traces for detection engineering, while depth data across scoring bands provides ground truth for prioritization validation. A tiered knowledge architecture accumulates intelligence across assessments, transferring build and exploitation experience to subsequent CVEs. Evaluation on 603 CVEs from the CVE-GENIE dataset achieves 67.8% end-to-end L1+ exploitation at USD 1.50 per CVE across eight languages and 187 CWE types. Exploitation rates remain near 68% regardless of EPSS or CVSS band, indicating that pattern-level reachability is orthogonal to metadata-based prioritization. Detection rules from L2+ exploitation achieve significantly higher span-normalized grounding than L1-derived rules (p=0.035), and 93.4% of generated Snort rules produce zero false positives against a synthetic benign corpus.
Bagus Rakadyanto Oktavianto Putra, Muhamad Risqi U. Saputra, Widyawan, Guntur Dharma Putra
Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation and demand unnecessarily massive computational overhead. In this study, we introduce an efficient end-to-end smart contract security audit framework utilizing lightweight, highly optimized open-source LLMs (0.6B-4B parameters). Our framework decouples comprehensive audit tasks into four interconnected components: vulnerability detection, explanation, severity classification, and remediation recommendation. To maintain high accuracy without massive parameters, we implement Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a custom Chain-of-Verification (CoVe) aggregation strategy to systematically screen and consolidate multiple draft responses from the model into a highly accurate audit report. Experimental results demonstrate that our lightweight pipeline consistently outperforms state-of-the-art open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability detection and an alignment score of 0.4375 in generative explanation tasks. Furthermore, our extensive ablation studies empirically validate the superiority of our decoupled audit processes over unified prompting and uncover a novel severity centrality bias, establishing a critical benchmark for future research in LLM-assisted auditing.
Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We introduce SCDBench, a dataset and benchmark methodology for LLM-based smart contract decompilation. The dataset contains 600 real-world Solidity contracts with paired bytecode inputs, ground-truth source code, and replayable semantic checkpoints. SCDBench evaluates decompiler outputs through four cumulative stages: format completeness, compilability, Application Binary Interface (ABI) recovery, and semantic consistency via differential replay. We evaluate Claude Opus 4.7, GPT-5.3-Codex, and GLM-5 in a zero-shot decompilation setting, including GLM-5 variants with and without extended reasoning and a zero-shot compilation-repair setting. The results show that frontier LLMs can often produce structured and compilable Solidity, but achieving semantic consistency remains far from solved: the best-performing frontier model perfectly decompiles only 42/600 contracts. We further show that introducing same-model compilation repair substantially improves performance at modest additional cost. SCDBench establishes a common ground for rigorous, reproducible evaluation and aims to accelerate the development of reliable smart contract decompilers for blockchain security and transparency.
Since 2016, Apple has claimed that device analytics collected to improve user experience are protected by differential privacy (DP). Apple's DifferentialPrivacy framework is deployed across its operating systems and handles sensitive signals such as Safari domains, keyboard events, photo attributes, and health-related reports. Because Apple has not open-sourced its privatization algorithms, these privacy claims have been difficult to verify independently. We present a client-side audit of Apple's DP framework on macOS Sonoma 14.2 and Sequoia 15.6. We reverse engineer the shipped binaries, recover Objective-C interfaces, build runtime harnesses that execute Apple's deployed mechanisms, and test whether their outputs match the advertised privacy guarantees. Our audit covers nearly all active deployed mechanisms, including Count Median Sketch, Hadamard-CMS, randomized-response mechanisms, and Prio-style secure aggregation. We find multiple implementation bugs and misconfigurations. Every audited mechanism that relies on floating-point noise fails to meet its advertised DP or zero-knowledge proof guarantee, due to insecure samplers with known floating-point vulnerabilities. We also find secure-aggregation configurations with local DP disabled, exposing pre-aggregation records to any party with access to those logs. Overall, we find DP violations in 5 of 9 audited mechanisms, affecting 87% of data collection in macOS Sonoma and 68% in Sequoia. We also identify public leaked iPhone logs that can be decoded to recover private information, including Safari domains and keyboard emoji signals.
Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara
Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close this gap, but each class has a characteristic failure mode. Static analyzers report findings that frequently fail manual triage at high rates, while large language models (LLMs) hallucinate findings that contradict the source code. Thus, we propose Chaintrix, an end-to-end auditing framework whose central architectural commitment is that every LLM-generated claim must be discharged against a deterministic structural contract representation. We introduce a Cross-Contract Interaction Model (CCIM) that parses Solidity into a structured map of function-level reads, writes, modifiers and resolved cross-contract calls. CCIM serves as the substrate against which all 12 of Chaintrix's deterministic signal engines and the parallel LLM audit pipelines operate. A staged false-positive-reduction pipeline, terminating in a Structural Verdict Engine (SVE) that applies deterministic structural checks against parsed code, filters the merged finding set, with selected high-confidence findings further validated through symbolic execution and fuzz testing. We evaluate Chaintrix on EVMbench, the smart-contract security benchmark by OpenAI, Paradigm, OtterSec. Chaintrix detects 86 of 120 high-severity vulnerabilities (71.7% recall), with 25 audits scoring 100% recall, placing Chaintrix 26 percentage points above the strongest frontier-model baseline.
Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions: foundation-model-based smart contract semantics and vulnerability reasoning [1], automated smart contract repair with formal guarantees [2], adversarial learning for robust malicious contract and transaction detection [3], and real-time transaction-level exploit detection at blockchain scale [4]. We connect these directions to two recent studies that characterize the current frontier: a diagnostic analysis of where smart contract security analyzers fall short [5] and a scalable real-time system for malicious Ethereum transaction detection [6]. The resulting framework is intended to help students formulate capstone projects that are technically grounded, empirically measurable, and aligned with contemporary smart contract security research.
Smart contracts on blockchains are prone to diverse security vulnerabilities that can lead to significant financial losses due to their immutable nature. Existing detection approaches often lack flexibility across vulnerability types and rely heavily on manually crafted expert rules. In this paper, we present an LLM-based framework for practical smart contract vulnerability detection. We construct and release a large-scale dataset comprising 31,165 professionally annotated vulnerability instances collected from over 3,200 real-world projects across 15 major blockchain platforms. Our approach leverages precise AST-based context extraction and vulnerability-specific prompt design to instantiate customized detectors for 13 prevalent vulnerability categories. Experimental results demonstrate strong effectiveness, achieving an average positive recall of 0.92 and an average negative recall of 0.85, highlighting the potential of carefully engineered contextual prompting for scalable and high-precision smart contract security analysis.
Smart contract vulnerabilities in Decentralized Finance caused over billions of dollars losses every year, yet the security community faces a critical bottleneck: identifying a vulnerability is not the same as proving it is exploitable. Manual PoC construction is prohibitively labor-intensive, leaving most disclosed vulnerabilities unverified and protocols exposed long before mitigation is applied. In this paper, we propose \sys, a knowledge-driven agentic system for end-to-end contract vulnerability detection and exploit synthesis. Our core insight is that exploit synthesis is not a code generation task but a \emph{structured reasoning problem} that requires grounded knowledge of protocol semantics, failure root cause, and exploit primitives. \sys organizes this knowledge into a \emph{Hierarchical Knowledge Graph} (HKG) that serves as structured memory for LLM-guided multi-hop reasoning. To validate exploit feasibility beyond code synthesis, \sys employs a two-stage validation framework that checks exploit-path reachability via SMT solving and profit realizability via asset-level state simulation, ensuring generated PoCs satisfy both logical and economic viability constraints. Evaluated on 88 real-world DeFi attacks and 72 audited projects (2,573 contracts), \sys achieves 98\% recall and 0.9 F1-score in detection, and a 96.6\% exploit success rate (ESR), reproducing 85 historical exploits and recovering over \$116.2M revenue. \sys outperforms SOTA fuzzers (\textsc{Verite}, \textsc{ItyFuzz}) by up to $5\times$ in ESR and $300\times$ in recoverable value, and the LLM-based exploit generator \textsc{A1} by $2\times$ and $8.5\times$ respectively. In bug bounty evaluation, \sys identified 16 confirmed 0-day vulnerabilities, helping secure over \$70.6M and earning \$2,900 in bounties.
Shereen Ismail, Bashar Othman, Hassan Reza, Eden Teshome Hunde
Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry.
Smart contracts are a core component of blockchain-based systems, enabling decentralized applications to autonomously manage assets and enforce program logic. However, vulnerabilities in smart contracts can cause severe financial losses because of their immutability and public accessibility. As a result, analyzing common attack vectors and evaluating static detection techniques before deployment remain critical challenges in blockchain security. This thesis presents an analytical study of high-impact smart contract attack classes and evaluates the effectiveness of AST-based static detection approaches for Ethereum smart contracts. Building on the Aderyn static analysis framework, custom detectors are implemented to analyze structural code patterns that enable reentrancy attacks, authorization bypass vulnerabilities, and unsafe proxy delegation and storage collision risks. Rather than proposing new defensive mechanisms, this work systematically analyzes how known attacks arise from insecure smart contract programming practices and examines how defensive coding patterns can be identified at the source-code level through static analysis. The effectiveness of the analysis is evaluated using vulnerable smart contract implementations and corresponding exploit scenarios developed with the Foundry testing framework. Experimental results show that the implemented detectors successfully identify exploitable vulnerabilities and demonstrate a strong correspondence between attack-enabling code structures and statically detectable patterns, confirming alignment between static analysis findings and real-world attack behavior. This work demonstrates that extensible AST-based static analysis provides a practical foundation for analyzing both smart contract attacks and the defensive patterns intended to mitigate them before deployment.
As blockchain ecosystems grow, financially motivated attackers increasingly exploit decentralized finance (DeFi) protocols, causing frequent and severe losses. Unlike conventional cyberattacks, DeFi exploits propagate rapidly due to the transparent and composable nature of smart contracts. We identify a critical pattern, Imitative Attack Cascade: an initial successful exploit is quickly followed by mimicking transactions that reuse attack logic with minor modifications or parameter changes. Our empirical analysis shows that over 69% of DeFi attacks exhibit strong behavioral similarity to earlier incidents, often within hours or days of the initial attack. This exposes a fundamental limitation in current reactive detection. Initial attacks are typically flagged via heuristic alerts (Tornado Cash traces, anomalous nonce usage, exploiter labels), but turning these signals into detection rules requires manual validation and handcrafted trace analysis -- a labor-intensive, slow process that leaves follow-up attacks to spread. Our goal is to ensure that once an attack has been observed, even a single instance, it can be rapidly abstracted into an actionable, generalizable detection rule. We decompose the problem into two challenges: (I) abstracting the semantics of diverse, obscure function signatures, and (II) matching transaction logic in noisy, evasive traces. We leverage two insights: (i) the open-source nature of most DeFi protocols enables high-fidelity semantic classification of function signatures; (ii) contract labels isolate essential logic by filtering irrelevant calls and classifying attack intent. Building on these, we develop GenDetect, which achieves ACC 98%, FPR 1%, FNR 3% and discovers 56 previously unrevealed attacks from the past three years. Source code and dataset: https://github.com/NobodyIsAnonymous/GenDetect_ICSE2026
Phishing attacks pose a significant security issue in Ethereum-based blockchain systems. Existing solutions, like TEGDetector, address these attacks by analysing how transactions evolve over time using Transaction Evolution Graphs (TEGs) constructed via time slicing, followed by a dynamic graph classifier that captures both spatial structure and temporal evolution with learned time coefficients. However, building and managing these graphs across multiple stages makes the overall approach complex and difficult to implement. In this work, we propose E2E-EmbedDetector, a lightweight end-to-end neural classification model that works directly with raw transaction data. The model learns embedding representations for important entities such as From, To, and ContractAddress, and also used two additional numeric features: transactional value and a derived input length. We train and evaluate the model on a balanced dataset of 50,000 Ethereum transaction using an 80/20 stratified split. The model achieves an accuracy of 95.63%, precision of 0.9265, recall of 0.9912, an F1 score of 0.9578, a ROC-AUC score of 0.9915 and a PR-AUC score of 0.9909. These results show that strong phishing can be achieved using a simpler and more practical tabular approach, without relying on complex temporal graph- based networks.
Security vulnerabilities in smart contracts can lead to serious economic losses, while traditional detection methods have limitations in coarse-grained labeling and single graph structure representation. We propose an improved GraphCodeBERT based multi granularity and multi graph fusion smart contract vulnerability detection method. Firstly, propose a vulnerability multi granularity annotation (MA) module that includes file level, function level, and statement level to address the issue of fine-grained annotation. Secondly, based on the data flow graph encoded by GraphCodeBERT, further design a multi graph fusion representation learning (MGRL) strategy. This strategy integrates control flow graph and function call graph, captures code semantics from control dependency and call dependency dimensions through graph attention network and graph convolutional network, to comprehensively enhance the representation capability of complex vulnerabilities. The detection results of four smart contract vulnerabilities show that our method outperforms mainstream methods such as Slither, CodeBERT, Devign, and GraphCodeBERT in accuracy, recall, and F1 score. Especially in sentence level detection tasks, the F1 score reached 81.03%, an increase of 7.31% compared to the baseline, and the false positive rate decreased to 8.15%.
Behkish Nassirzadeh, Rui Xi, Karthik Pattabiraman, Vasisht L. Ganesh
Blockchain technologies have experienced rapid adoption across various sectors, including supply chain management, decentralized finance and cross-border payments. With this growth, however, the complexity and security risks of maintaining blockchain integrity and functionality have increased. Addressing these challenges requires a systematic and rigorous organization of knowledge in blockchain security. This paper presents a Systematization of Knowledge (SoK) study based on a structured survey of academic literature, industry reports and real-world case studies. The authors classify vulnerabilities into three layers: system-level, smart contract-level and application-level, analyzing their root causes, real-world prevalence and mitigation tradeoffs. The taxonomy encompasses blockchain-specific threats (e.g. gas-based DoS attacks, MEV) as well as vulnerabilities inherited from distributed systems and software (e.g. Sybil attacks, access control failures). The authors critically evaluate detection and mitigation techniques, including static and dynamic analysis, fuzzing, symbolic execution and formal verification, assessing their precision, recall, scalability and inherent limitations. The authors further review state-of-the-art auditing tools in terms of methodology, adoption and shortcomings. Finally, the authors discuss advanced approaches such as hybrid frameworks that combine AI with program analysis, SMT solvers, and zero-knowledge proofs, outlining how these can address current gaps in scalability, interpretability and runtime verification. Overall, this study systematizes the security landscape of blockchain technologies, synthesizes the limitations of current approaches, and identifies technically actionable future research directions toward building more robust and resilient blockchain systems.
Introduction The proliferation of dApps is increasing the attack surface for exploitable vulnerabilities in smart contracts, and thus there is a need for verifiable detection methodologies. Methods In this work, we propose a machine learning framework with blockchain integration for explainable and note that “explainable” implies “verifiable” smart contract vulnerability detection. The SmartBugs-curated data was systematically pre-processed with metadata filtering, feature correlation analysis and encoding for model evaluation. Four ensemble learning methods, Random Forest, XGBoost, LightGBM and CatBoost were tested under identical experimental settings for comparison. Results The Random Forest classifier initially achieved the best balance in terms of stability and performance with an accuracy of 87.67%, successfully detecting important vulnerability classes such as re-entrancy, unchecked low-level calls, etc. To enhance the applicability of our blockchain-based machine learning framework for vulnerable smart contract analysis we extend it from the initial 143-contract dataset SmartBugs-Curated to evaluate it on on large-scale set, namely, SmartBugs-Wild which contains 47,398 real-world Ethereum contracts. Based on 29 static contract-level features, unsupervised clustering (k = 4, silhouette score = 0.3735) identifies discrete structural archetypes present in the dataset. Ensemble classifiers (such as XGBoost, CatBoost, Random Forest and LightGBM) can get excellent discriminative performance on these cluster labels: LightGBM achieves 99% accuracy and 0.98918 macro-F1. Discussion The additional results show that the approach scales, is robust and leads to stable models, even if interpretable. After injecting SHAP-based explainability, the interpretability and predictive power of CatBoost became similar to those of Random Forest. In order to guarantee end-to-end trust and traceability of our optimised classifier, this was linked to a blockchain oracle that independently store the outcomes as well as confidence scores for predictions directly onto an Ethereum-compatible ledger through a Vulnerability Registry smart contract. This integration provides the data is immutable, auditable and transparent in reporting.
Detecting vulnerabilities in smart contracts is a critical challenge for blockchain security, as flaws such as reentrancy, timestamp dependence, and infinite loops have resulted in severe financial losses in decentralized systems. Accurate and interpretable detection of these vulnerabilities remains challenging due to the complex semantics of smart contract code. In this study, we propose a multimodal hybrid recurrent framework for smart contract vulnerability detection that integrates sequential and structural code representations. The framework introduces a Selective Subpattern Activation (SSA) mechanism, which highlights vulnerability-indicative code subpatterns during the pattern extraction phase and provides interpretable insights into model predictions. Pattern-based features enhanced by SSA are processed using a Bidirectional Gated Recurrent Unit (BiGRU), while structural features derived from control and data flow representations are modeled using a Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed approach is evaluated on a publicly available Ethereum smart contract dataset using five independent experimental runs, with results reported as averages. The results show that the framework achieves an accuracy of 92.16% and an F1 score of 88.83% for reentrancy vulnerability detection, achieving higher performance compared to baseline deep learning and graph-based models. Ablation experiments are performed to demonstrate the contribution of the SSA mechanism to both detection performance and interpretability.
Permission control vulnerabilities in Non-fungible token (NFT) contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or circumvent critical permission checks. In this paper, we propose NFTDELTA, a framework that leverages static analysis and multi-view learning to detect permission control vulnerabilities in NFT contracts. Specifically, we extract comprehensive function Control Flow Graph (CFG) information via two views: sequence features (representing execution paths) and graph features (capturing structural control flow). These two views are then integrated to create a unified code representation. We also define three specific categories of permission control vulnerabilities and employ a custom detector to identify defects through multi-view feature similarity analysis. Our evaluation of 795 popular NFT collections identified 241 confirmed permission control vulnerabilities, comprising 214 cases of Bypass Auth Reentrancy, 15 of Weak Auth Validation, and 12 of Loose Permission Management. Manual verification demonstrates the detector's high reliability, achieving an average precision of 97.92% and an F1-score of 81.09%. Furthermore, NFTDELTA demonstrates enhanced efficiency and scalability, proving its effectiveness in securing NFT ecosystems.
Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Although numerous tools have been developed for detecting smart contract vulnerability, their effectiveness remains limited, particularly due to the high false positives included in the reported results. Therefore, developers and auditors are often overwhelmed with manually verifying the reported issues. A fundamental reason behind this is that while a reported vulnerability satisfies specific vulnerable patterns, it may not actually be exploitable, either because the vulnerable code cannot be triggered or it does not result in any financial loss. In this paper, we propose V2E, a new framework for validating whether a reported vulnerability is truly exploitable. The core idea of V2E is to automatically generate executable Proof-of-Concept Exploit (PoC for short), and then assess if the vulnerability could be triggered and incur any real damage (i.e., causing financial loss) by the PoC. While LLMs have shown proficiency in PoC generation, achieving our task is by no means trivial. In detail, it is difficult for LLM to: (1) generate and update PoC to trigger a specific vulnerability, (2) evaluate the PoC’s effectiveness to validate exploitable vulnerability. To this end, V2E automates the whole process through a novel combination of PoC generation, validation, and refinement: (1) Firstly, V2E generates targeted PoCs by analyzing potential vulnerability paths. (2) Then, V2E verifies the validity of PoCs through triggerability and profitability analysis. (3) In addition, V2E iteratively refines the generated PoC based on PoC execution feedback, therefore, increasing the chance to confirm the vulnerability. Evaluation on 264 manually labeled contracts shows that V2E outperforms the baseline approach. Particularly, V2E successfully identifies 102 out of 124 exploitable vulnerabilities, achieving a precision of 91.9% and a recall of 82.3%. In addition, it successfully eliminates 71 out of 140 false alarms (50.7%). Besides, V2E effectively enhances the performance of SOTA tools. It reduces the false positive rates of Slither by 76.9%, Mythril by 56.9% and Confuzzius by 65%.
Gregorio Dalia, Tat Luat Nguyen, Andrea Di Sorbo, Corrado Aaron Visaggio · 5 authors
Ethereum smart contracts manage billions in digital assets, and vulnerability detection is critical given the immutability of deployed code and the irreversible nature of transactions. However, existing tools such as Slither rely on rigid, rule-based analysis, and general-purpose language models like ChatGPT often miss rare or context-dependent bugs. To address these limitations, this paper presents BreachT5, an ensemble of two fine-tuned CodeT5+ models designed for multi-label vulnerability detection in Solidity contracts. We first fine-tune a 220M parameter model on over 67,000 real contracts labeled with the Smart Contract Weakness Classification (SWC), revealing intrinsic detection differences across vulnerability types. We then explore the performance of a 770M variant, which improves accuracy on frequent classes but underperforms on rare ones. To balance this trade-off, BreachT5 combines both models via soft voting with per-class thresholds. Our results on the BCCC-SCsVuls2024 dataset show that BreachT5 achieves 0.556 Macro-F1 and 0.612 Micro-F1, outperforming the two standalone models, Slither, and GPT-5 in multi-label vulnerability detection.
Decentralized applications (DApps) represent a paradigm shift in software architecture, leveraging blockchain technology and distributed consensus mechanisms to eliminate single points of failure and centralized control. As the adoption of DApps accelerates across sectors such as finance, supply chain, healthcare, and governance, ensuring their functional correctness and behavioral reliability has become a critical engineering challenge. Unlike traditional software, DApps operate in adversarial, permissionless environments where smart contracts execute autonomously and immutably on distributed nodes, making post-deployment correction extremely costly or impossible. This review systematically examines the landscape of functional testing methodologies tailored for decentralized applications, analyzing their suitability, limitations, and practical applicability in modern DApp development workflows. We survey research spanning smart contract verification, consensus protocol testing, oracle interaction validation, cross-chain interoperability testing, and user-layer functional testing of Web3 interfaces. The review identifies four dominant testing paradigms: (1) unit testing of smart contract functions, (2) integration testing of DApp components, (3) property-based testing using formal specifications, and (4) end-to-end simulation on testnets. Through comparative analysis across 13 seminal studies, we evaluate each approach along dimensions of automation feasibility, coverage depth, gas efficiency awareness, and scalability to complex DApp ecosystems. Our findings indicate that while static analysis and symbolic execution tools such as Mythril, Slither, and Manticore offer strong vulnerability detection, they address security properties more than functional correctness. Conversely, framework-based testing tools like Hardhat, Truffle, and Foundry provide adequate unit-level coverage but struggle with cross-contract orchestration and event-driven logic verification. A critical gap exists in testing oracle-dependent and DAO governance workflows. This review concludes with a synthesis of best practices, open research challenges, and a directional roadmap for developing holistic functional testing frameworks suited to the evolving complexity of decentralized systems.