Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.
Abstract
The widespread adoption of web applications has significantly increased exposure to security risks, making automated
vulnerability assessment an essential component of modern software development. Although several open-source scanners
provide strong detection capabilities, they typically operate independently, require technical expertise, and produce unstructured
reports, limiting their accessibility and effectiveness. This paper introduces HawkEye, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard.
HawkEye uses Docker-isolated environments to orchestrate scanners and employs a Django–SQLite3 backend to manage scan
workflows and result storage. The current implementation provides full integration for OWASP ZAP and Nikto, while modular
support is included for incorporating Nmap, SQLMap, and Wapiti. The platform consolidates heterogeneous outputs into a
standardized schema, applies CVSS-based severity classification, and generates exportable PDF/JSON reports with
remediation guidance. Experimental testing on intentionally vulnerable applications demonstrates HawkEye’s ability to detect
prominent security weaknesses and illustrates how consolidated reporting improves vulnerability prioritization for development
teams
An extensible platform capable of orchestrating reconnaissance, dynamic application security testing (DAST), static configuration analysis, and information leak detection through unified command-line interface and consolidated HTML reporting is developed.
O.I. Fediushyn, Hlib Khivrenko, Nataliia Popova et al.· Terra security· 0 citations
The findings confirm that an accessible, accurate, and AI-augmented vulnerability scanner can be constructed for educational and entry-level use within a deliberately bounded scope, lowering the expertise barrier for web application security assessment.
Automated Web Application Testing plays a vital role in identifying vulnerabilities, analyzing attacks, and generating cybersecurity documentation for modern web environments. Artificial Intelligence (AI) significantly enhances web application testing performance by enabling adaptive threat detection, intelligent payload verification, anomaly-based response evaluation, and automated threat documentation. These AI-driven capabilities reduce manual effort while improving the accuracy, scalability, and reliability of vulnerability assessment and security reporting. However, existing automated web testing frameworks suffer from fragmented workflows, lack of contextual awareness, inefficient vulnerability prioritization, excessive false positive generation, and limited adaptive scanning capabilities. The framework integrates adaptive reconnaissance, vulnerability analysis, and automated documentation within a unified security assessment workflow. The framework introduces the Adaptive Reconnaissance Intelligence Engine (ARIE) to intelligently discover attack surfaces and perform dynamic reconnaissance analysis. A Secure Threat Enumeration Mechanism (STEM) is designed to conduct automated multilayer vulnerability scanning and threat correlation across web components. Furthermore, the Deep Response Payload Analyzer (DRPA) utilizes behavioral response correlation intelligence to detect SQL Injection and Cross-Site Scripting (XSS) attacks through contextual payload-response analysis. In addition, the Cognitive Risk Documentation Framework (CRDF) automates vulnerability severity assessment, exploit validation, and structured cybersecurity report generation for efficient threat documentation. The proposed modular workflow enables a systematic transition from reconnaissance to vulnerability validation and automated risk documentation. Experimental evaluation demonstrates that the proposed framework achieves accurate vulnerability detection, reliable adaptive scanning, automated attack validation, and reduced false positive rates. The system provides an intelligent and efficient cybersecurity evaluation and documentation support framework for modern web application environments.
Arun K S, Abhiram S, Ageesh Lal N G et al.· 2026 6th International Confe...· 0 citations
VulnScanner Pro is a self-hosted assessment tool that tries to sit in the gap between the two extremes of manual penetration testing and automated security assessment, and recommends four recommendations for fixing the six missing HTTP security headers.
Athili Laxmi Lavanya, Mycharla Madhavkumar, D. Gangadhar· International Journal for Re...· 0 citations
Large Language Models (LLMs) are now deployed at an unprecedented scale across many critical sectors, rapidly transitioning from experimental AI tools to embedded components of production software systems. This accelerated adoption, often enabled by low-code integrations, has lowered technical barriers while simultaneously expanding the attack surface of modern applications, particularly when deployments occur without sufficient domain-specific security expertise. In many cases, security maturity has not progressed at the same pace as capability expansion, creating systemic exposure across confidentiality, integrity, and availability dimensions. To provide structured clarity amid this rapid growth, this paper presents a comparative and standards-aligned analysis of LLM security risks and defense mechanisms grounded in the OWASP GenAI Top-10 (2025). We systematically examine each vulnerability class, map representative attack patterns to primary mitigation strategies, evaluate their security property impact, and analyze practical limitations and implementation trade-offs. In addition, we introduce a severity-based assessment to prioritize risks according to operational and systemic impact, offering a quantitative perspective on defensive readiness. Our findings indicate that current mitigation strategies are predominantly reactive, concentrated at inference time, and unevenly distributed across the LLM lifecycle. Controls addressing training pipelines, supplychain dependencies, and autonomous system behaviors remain comparatively less mature and less standardized. By integrating vulnerability classification, defense mapping, severity prioritization, and trade-off analysis within a unified framework, this study provides actionable guidance for strengthening secure, resilient, and standards-driven LLM deployment in high-stakes environments.
Md Abdul Barek, Md Bajlur Rashid, A. K. I. Riad et al.· Annual International Compute...· 0 citations
The security of open-source cloud platforms is paramount, given their widespread adoption in modern business operations. This study conducts a large-scale empirical security analysis of the web-based management interfaces of ten widely used open-source Infrastructure-as-a-Service (IaaS) platforms. Using a black-box penetration testing methodology that combines automated scanning with Burp Suite and its security extensions with systematic manual analysis, we identified a total of 16 vulnerabilities spanning nine classes, including high-severity flaws that enable account takeover. Our findings, which culminated in the assignment of six new Common Vulnerabilities and Exposures (CVE) IDs, provide concrete, empirically validated examples of how server misconfigurations and flawed authentication logic manifest in the exposed management interfaces of mainstream open-source IaaS platforms, including mis-issued API tokens, time-based user enumeration, and host-header–driven misrouting. Specifically, the identified vulnerabilities stem from improper authentication, insufficient input validation, and server misconfigurations. In addition to a responsible vulnerability disclosure process, this work provides practical hardening recommendations to mitigate the identified weaknesses.
Alexandros Perrakis, Efstratios Chatzoglou, Vyron Kampourakis et al.· International Journal of Inf...· 0 citations
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