Search Results (74)

In order to solve the problem of traditional methods not being able to discover hidden attack trajectories, we propose a cyber attack path prediction approach based on a text-enhanced graph attention mechanism in this paper. Specifically, we design an ontology that captures multi-dimensional links between vulnerabilities, weaknesses, attack patterns, and tactics by integrating CVE, CWE, CAPEC, and ATT&CK into Neo4j. Then, we inject natural language descriptions into the attention mechanism to develop a text-enhanced GAT that can alleviate data sparsity. The experiment shows that compared with existing baselines, our approach improveds MRR and Hits@5 by 12.3% and 13.2%, respectively. Therefore, the proposed approach can accurately predict attack paths and support active cyber defense. Full article

Codeine, an opioid analgesic present in many over-the-counter (OTC) formulations, is frequently misused through non-medical extraction techniques such as cold water extraction (CWE). These practices carry substantial risks, including incomplete removal of hepatotoxic co-formulants, contamination, and highly unpredictable dosing. We report a fatal case of a 29-year-old man who ingested codeine extracted from Antidol^® tablets in combination with energy drinks and psychotropic medications. Post-mortem LC–MS/MS analysis revealed the presence of codeine (0.66 µg/mL), morphine (0.02 µg/mL), hydroxyzine (2.52 µg/mL), alprazolam (0.15 µg/mL), paracetamol (30.64 µg/mL), and additional substances in blood samples. Concentrations of codeine and hydroxyzine exceeded therapeutic ranges and were consistent with values reported in fatal intoxications, confirming a poly-drug poisoning. This case highlights the danger associated with non-medical codeine use, particularly when combined with central nervous system (CNS) depressants, and underscores the need for stricter regulation of OTC codeine-containing products as well as improved public awareness of the risks associated with domestic extraction methods. Full article

With the increasing complexity of software systems, the number of security vulnerabilities contained within software has risen accordingly. The existing shift-left security concept aims to detect and fix vulnerabilities during the software development cycle. While CodeQL stands as the premier static code analysis tool currently available on the market, its high barrier to entry poses challenges for meeting the implementation requirements of shift-left security initiatives. While large language model (LLM) offers potential assistance in QL code development, the inherent complexity of code generation tasks often leads to persistent issues such as syntactic inaccuracies and references to non-existent modules, which consequently constrains their practical applicability in this domain. To address these challenges, this paper proposes CQLLM (CodeQL-enhanced Large Language Model), a novel framework for automating the generation of CodeQL security vulnerability detection code by leveraging LLM. This framework is designed to enhance both the efficiency and the accuracy of automated QL code generation, thereby advancing static code analysis for a more efficient and intelligent paradigm for vulnerability detection. First, retrieval-augmented generation (RAG) is employed to search the vector database for dependency libraries and code snippets that are highly similar to the user’s input, thereby constraining the model’s generation process and preventing the import of invalid modules. Then, the user input and the knowledge chunks retrieved by RAG are fed into a fine-tuned LLM to perform reasoning and generate QL code. By integrating external knowledge bases with the large model, the framework enhances the correctness and completeness of the generated code. Experimental results show that CQLLM significantly improves the executability of the generated QL code, with the execution success rate improving from 0.31% to 72.48%, outperforming the original model by a large margin. Meanwhile, CQLLM also enhances the effectiveness of the generated results, achieving a CWE (Common Weakness Enumeration) coverage rate of 57.4% in vulnerability detection tasks, demonstrating its practical applicability in real-world vulnerability detection. Full article

Common Weakness Enumerations (CWEs) and Common Vulnerabilities and Exposures (CVEs) are open knowledge bases that provide definitions, descriptions, and samples of code vulnerabilities. The combination of Large Language Models (LLMs) with vulnerability knowledge bases helps to enhance and automate code vulnerability repair. Several key factors come into play in this setting, including (1) the retrieval of the most relevant context to a specific vulnerable code snippet; (2) augmenting LLM prompts with the retrieved context; and (3) the generated artifact form, such as a code repair with natural language explanations or a code repair only. Artifacts produced by these factors often lack transparency and explainability regarding the rationale behind the repair. In this paper, we propose an LLM-enabled framework for explainable recommendation of vulnerable code repairs with techniques addressing each factor. Our method is data-driven, which means the data characteristics of the selected CWE and CVE datasets and the knowledge base determine the best retrieval strategies. Across 100 experiments, we observe the inadequacy of the SOTA metrics to differentiate between low-quality and irrelevant repairs. To address this limitation, we design the LLM-as-a-Judge framework to enhance the robustness of recommendation assessments. Compared to baselines from prior works, as well as using static code analysis and LLMs in zero-shot, our findings highlight that multifaceted LLMs guided by retrieval context produce explainable and reliable recommendations under a small to mild level of self-alignment bias. Our work is developed on open-source knowledge bases and models, which makes it reproducible and extensible to new datasets and retrieval strategies. Full article

Carum carvi, commonly known as caraway, is a medicinal and culinary plant recognized for its anti-inflammatory properties, primarily attributed to its essential oil components. However, the thermogenic potential of caraway—particularly the biological activity of its water-soluble extract—remains largely unexplored. In this study, we investigated the effects and underlying mechanisms of caraway on Ucp-1 mRNA expression in beige adipocytes and on inflammation-mediated suppression of thermogenesis, by treating C3H10T1/2 adipocytes with caraway water extract (CWE) or caraway hexane extract (CHE) during both the induction and maturation phases, followed by isoproterenol stimulation, and measurement of mRNA levels of Ucp-1 and differentiation-related genes. Additionally, RAW264.7 cells were treated with CWE prior to stimulation with lipopolysaccharides followed by evaluation of inflammatory marker expression. CWE increased Ucp-1 mRNA expression directly by enhancing adrenergic sensitivity and promoting beige adipocyte differentiation during the induction phase of differentiation. Further, CWE mediated an indirect effect on Ucp-1 expression by suppressing macrophage inflammation, thus restoring Ucp-1 expression otherwise inhibited under inflammatory conditions. These results suggest that caraway extracts—especially the water-soluble compounds—may serve as therapeutic candidates for obesity-related conditions by enhancing energy expenditure and mitigating chronic inflammation. Full article

Public CVE feeds add tens of thousands of entries each year, overwhelming patch-management capacity. We model the CWE–CVE–CPE triad and, for each CWE, build count-weighted product co-exposure graphs by projecting CVE–CPE links. Because native graphs are highly fragmented, we estimate graph-distance box-counting dimensions component-wise on the fragmented graphs using greedy box covering on unweighted shortest paths, then assess significance on the largest component of reconnected graphs. Significance is evaluated against degree-preserving nulls, reporting null percentiles, a z-score–based p-value, and complementary KS checks. We further characterise meso-scale organisation via normalized rich-club coefficients and k-core structure. Additionally, we quantify percolation sensitivity on the reconnected graphs by contrasting targeted removals with random failures for budgets of 1%, 5%, 10%, and 20%. This quantification involves tracking changes in largest-component size, average shortest-path length on the LCC, and global efficiency, and an amplification factor at 10%. Our corpus covers the MITRE CWE Top 25; we report high-level summaries for all 25 and perform the deepest null-model and sensitivity analyses on a subset of 12 CWEs selected on the basis of CVE volume. This links self-similar topology on native fragments with rich-club/core organisation and disruption sensitivity on reconnections, yielding actionable, vendor/software-type-aware mitigation cues. Structural indices are used descriptively to surface topological hotspots within CWE-conditioned product networks and are interpreted alongside, not in place of, EPSS/KEV/CVSS severity metrics. Full article

Hardware systems are foundational to critical infrastructure, embedded devices, and consumer products, making robust security assurance essential. However, existing hardware security standards remain fragmented, inconsistent in scope, and difficult to integrate, creating gaps in protection and inefficiencies in assurance planning. This paper proposes a unified, standard-aligned, and threat-validated taxonomy of Security Objective Domains (SODs) for hardware security assurance. The taxonomy was inductively derived from 1287 requirements across ten internationally recognized standards using AI-assisted clustering and expert validation, resulting in 22 domains structured by the Boundary-Driven System of Interest model. Each domain was then validated against 167 documented hardware-related threats from CWE/CVE databases, regulatory advisories, and incident reports. This threat-informed mapping enables quantitative analysis of assurance coverage, prioritization of high-risk areas, and identification of cross-domain dependencies. The framework harmonizes terminology, reduces redundancy, and addresses assurance gaps, offering a scalable basis for sector-specific profiles, automated compliance tooling, and evidence-driven risk management. Looking forward, the taxonomy can be extended with sector-specific standards, expanded threat datasets, and integration of weighted severity metrics such as CVSS to further enhance risk-based assurance. Full article

Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy. Full article

Centipedes (Chilopoda, Myriapoda) are crucial soil predators, yet their vulnerability to climate and land use change remains unexplored. We assess the impact of these drivers on Greek centipedes, identify current and future biodiversity hotspots, and evaluate the effectiveness of the Natura 2000 Network of protected areas for their conservation. We used an updated species occurrence database of Greek centipedes, derived from literature reviews and museum collections, and evaluated database completeness and geographic sampling biases. Species Distribution Models were employed to predict future distribution shifts under climate and land use change scenarios. Biodiversity hotspots were identified based on species richness (SR) and corrected-weighted endemism (CWE) metrics. We overlapped SR and CWE metrics against the Natura 2000 Network to assess its effectiveness. We found that sampling effort is highly heterogeneous across Greece. All species are projected to experience range contractions, particularly in the 2080s, with variation across scenarios and taxa. Current biodiversity hotspots are concentrated in the south Aegean islands and mainland mountain ranges, where areas of persistent high biodiversity are also projected to occur. The Natura 2000 Network currently covers 52% of SR and 44% of CWE hotspots, with projected decreases in SR coverage but increases in CWE coverage. Our work highlights the vulnerability of Greek centipedes to climate and land use change and reveals conservation shortfalls within protected areas. We identify priority areas for future field surveys, based on sampling bias and survey completeness assessments, and highlight the need for further research into mechanisms driving centipede responses to global change. Full article

Ball-end milling cutters are normally used for complex surface machining. During the milling process, the tool posture and cutting parameters of the ball-end milling cutters have a significant impact on chip formations and morphological changes. Based on the Cutter Workpiece Engagement (CWE) model, this study establishes a chip flow model for ball-end milling cutters with consideration of the tool posture variation. The machining experiments of Ti-6Al-4V with a 15° inclined plane and different feed directions were carried out. The influence mechanism of time-varying tool posture on chip formation was systematically investigated. The results reveal an interaction between the chip flow direction and the cutting velocity direction. The included angle between the chip flow directions at the maximum and minimum contact points in the CWE area affects the degree of chip curling, with a smaller angle leading to weaker curling. This research provides a theoretical foundation for the optimization of posture parameters of ball-end milling cutters and expounds on the influence of the chip flow angle on chip deformation. Full article

Objectives: New smartphone-based methods for measuring cervical spine range of motion (CS-ROM) and posture are emerging. The purpose of this study was to assess the reliability and validity of three such methods in patients with non-specific chronic neck pain (NSCNP). Methods: The within-day test–retest reliability of CS-ROM and forward head posture (craniovertebral angle-CVA) was examined in 45 patients with NSCNP. CS-ROM was simultaneously measured with an accelerometer sensor (KFORCE Sens^®) and a mobile phone device (iHandy and Compass apps), testing the accuracy of each and the parallel-forms reliability between the two methods. For construct validity, correlations of CS-ROM with demographics, lifestyle, and other cervical and thoracic spine biomechanically based measures were examined in 90 patients with NSCNP. Male–female differences were also explored. Results: Both methods were reliable, with measurements concurring between the two devices in all six movement directions (intraclass correlation coefficient/ICC = 0.90–0.99, standard error of the measurement/SEM = 0.54–3.09°). Male–female differences were only noted for two CS-ROM measures and CVA. Significant associations were documented: (a) between the six CS-ROM measures (R = 0.22–0.54, p < 0.05), (b) participants’ age with five out of six CS-ROM measures (R = 0.23–0.40, p < 0.05) and CVA (R = 0.21, p < 0.05), (c) CVA with two out of six CS-ROM measures (extension R = 0.29, p = 0.005 and left-side flexion R = 0.21, p < 0.05), body mass (R = −0.39, p < 0.001), body mass index (R = −0.52, p < 0.001), and chest wall expansion (R = 0.24–0.29, p < 0.05). Significantly lower forward head posture was noted in subjects with a high level of physical activity relative to those with a low level of physical activity. Conclusions: The reliability of both CS-ROM methods was excellent. Reductions in CS-ROM and increases in CVA were age-dependent in NSCNP. The significant relationship identified between CVA and CWE possibly signifies interconnections between NSCNP and the biomechanical aspect of dysfunctional breathing. Full article

Software attack surfaces define the external boundaries—entry points, communication channels, and sensitive data stores through which adversaries may compromise a system. This paper introduces a scoring mechanism that produces a normalized attack-surface metric in the range of 0–1. Building on the established Damage-Potential-to-Effort ratio, our approach further incorporates real-world vulnerability intelligence drawn from MITRE’s CVE and CWE repositories. We compute each application’s score by ingesting preliminary findings from a static-analysis tool and processing them through our unified model. To assess effectiveness, we validate the scoring system across a spectrum of scenarios, from a simple Java application to complex enterprise applications. The resulting metric offers development and security teams a concise, objective measure to monitor an application’s attack surface and hence proactively identify vulnerabilities in their applications. This tool can also be used to benchmark various third-party or dependent applications, enabling both developers and security practitioners to better manage risk. Full article

The increasing integration of digital technologies in healthcare has expanded the attack surface for privacy violations in critical systems such as electronic health records (EHRs), telehealth platforms, and medical device software. However, current vulnerability detection datasets lack domain-specific privacy annotations essential for compliance with healthcare regulations like HIPAA and GDPR. This study presents C3-VULMAP, a novel and large-scale dataset explicitly designed for privacy-aware vulnerability detection in healthcare software. The dataset comprises over 30,000 vulnerable and 7.8 million non-vulnerable C/C++ functions, annotated with CWE categories and systematically mapped to LINDDUN privacy threat types. The objective is to support the development of automated, privacy-focused detection systems that can identify fine-grained software vulnerabilities in healthcare environments. To achieve this, we developed a hybrid construction methodology combining manual threat modeling, LLM-assisted synthetic generation, and multi-source aggregation. We then conducted comprehensive evaluations using traditional machine learning algorithms (Support Vector Machines, XGBoost), graph neural networks (Devign, Reveal), and transformer-based models (CodeBERT, RoBERTa, CodeT5). The results demonstrate that transformer models, such as RoBERTa, achieve high detection performance (F1 = 0.987), while Reveal leads GNN-based methods (F1 = 0.993), with different models excelling across specific privacy threat categories. These findings validate C3-VULMAP as a powerful benchmarking resource and show its potential to guide the development of privacy-preserving, secure-by-design software in embedded and electronic healthcare systems. The dataset fills a critical gap in privacy threat modeling and vulnerability detection and is positioned to support future research in cybersecurity and intelligent electronic systems for healthcare. Full article

The development of inter-well channeling pathways has become a major challenge restricting the effectiveness of the thermal recovery process for heavy oil reservoirs, which leads to non-uniform sweep and reduced oil recovery. This is especially true for the characteristics of the higher injection–production intensity in offshore operations, making the issue more prominent. In this study, a quick and widely applicable approach is proposed for channeling identification, utilizing the static reservoir parameters and injection–production performance. The results show that the cumulative injection–production pressure differential (CIPPD) over the cumulative water equivalent (CWE) exhibits a linear relationship when connectivity exists between the injection and production wells. Thereafter, the seepage resistance could be analyzed quantitatively by the slope of the linear relationship during the steam injection process. Simultaneously, a channeling identification chart could be obtained based on the data of injection–production performance, dividing the steam flooding process into three different stages, including the energy recharge zone, interference zone, and channeling zone. Then, the established channeling identification chart is applied to injection–production data from two typical wells in the Bohai oilfield. From the obtained channeling identification chart, it is shown that Well X1 exhibits no channeling, while Well X2 exhibited channeling in the late stage of the steam flooding process. These findings are validated against the field performance (i.e., the liquid rate, water cut, flowing temperature, and flowing pressure) to confirm the accuracy. The channeling identification approach in this paper provides a guide for operational adjustments to improve the effect of the thermal recovery process in the field. Full article

As the world accelerates toward a sustainable future with electric vehicles (EVs), smartphone applications have become an indispensable tool for drivers. These applications, developed by both EV manufacturers and third-party developers, offer functionalities such as remote vehicle control, charging station location, and route planning. However, they also have access to sensitive information, making them potential targets for cyber threats. This paper presents a comprehensive survey of the cybersecurity vulnerabilities, weaknesses, and permissions in these applications. We categorize 20 applications into two groups: those developed by EV manufacturers and those by third parties, and conduct a comparative analysis of their functionalities by performing static and dynamic analysis. Our findings reveal major security flaws such as poor authentication, broken encryption, and insecure communication, among others. The paper also discusses the implications of these vulnerabilities and the risks they pose to users. Furthermore, we analyze 10 permissions and 12 functionalities that are not present in official EV applications and mostly present in third-party apps, leading users to rely on poorly built third-party applications, thereby increasing their attack surface. To address these issues, we propose defensive measures which include 10 CWE AND OWASP top 10 defenses to enhance the security of these applications, ensuring a safe and secure transition to EVs. Full article