Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1805-1813· 0 citations
TL;DR
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.
Abstract
Manual penetration testing needs specialist skills that most student developers and small teams simply don't have time
to build, and the commercial DAST products that automate this work — Burp Suite Professional, Acunetix, Nessus — are priced
for enterprise budgets rather than a college project or a solo developer's side application. Mature open-source alternatives such
as OWASP ZAP close the cost gap but not the usability one: getting useful output still means learning how to configure a scan
policy, spider a target, and read through a long list of raw findings. This paper describes VulnScanner Pro, a self-hosted
assessment tool that tries to sit in the gap between those two extremes. It runs eight checks against a target URL — SQL
injection, cross-site scripting, HTTP security-header analysis, SSL/TLS configuration, CSRF protection, WHOIS, IP
geolocation, and Nmap-based port scanning — behind a FastAPI backend and a Next.js dashboard, storing every scan in
SQLite so past results can be revisited later. A rule-based scoring function turns the combined module output into one 0–100 risk
number and a CRITICAL/HIGH/MEDIUM/LOW label, and a matching recommendation engine looks up remediation text and
reference links for whatever was found Active scanning of a system that you don’t own can be a legal problem as much as a
technical one. So the API by default won’t start a scan of a system unless you explicitly pass in a consent flag and some text
justification for why you are scanning the system. In a case study on a web application owned by the second author, the authors
ran the full eight-module automated security assessment. It took about a minute to run the assessment and it reported a risk
score of 20 out of 100 or LOW. The six checks for missing HTTP security headers were the only issues found. The checks for
SSL/TLS, SQL injection, Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF) all reported that they found no
issues. Only the two expected web ports (80 and 443) were open on the web application. The risk breakdown for the web
application is shown below along with four recommendations for fixing the six missing HTTP security headers. Each of the
recommendations are linked to authoritative external references for additional information on how to fix each of the issues
found
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.
D. R. Patil, Varad Salgare, Devaj Arya et al.· International Journal for Re...· 0 citations
The risk is not tied to any particular model but rather to the task category, and organizations should ask not which tool to trust, but instead whether LLM-generated automation code should be deployed without review.
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.
Cross-Site Scripting (XSS) remains a common and high-impact web vulnerability. Detecting XSS with high precision is difficult because exploitability depends on end-to-end data flow (sources, sanitizers, and sinks), framework-specific rendering semantics, and the correctness of context-aware output encoding. Although secure development lifecycles increasingly integrate static application security testing (SAST) into CI/CD pipelines, practical adoption is often limited by false positives and incomplete modeling of project-specific sanitization and templating behavior. In parallel, machine learning (ML) approaches show promise for learning vulnerability-relevant patterns from code, but they depend on the availability of realistic labeled data. This paper studies a low-cost repository-native signal that is routinely available but underused in ML-assisted static analysis: commit messages. We propose a mining and weak-labeling pipeline for Java repositories that identifies candidate XSS-fix commits using message heuristics grounded in OWASP guidance and CWE taxonomy, aligns commit messages with pre-/post-change code contexts extracted from diffs, and trains classifiers under two feature settings: (i) code-only and (ii) code+commit-message fusion. Across three model families (Random Forest, TextCNN, and BiLSTM), commit-message fusion consistently improves F1 and AUPRC and reduces false positives, suggesting that developer intent provides a useful auxiliary signal for security triage.
Abu Alam, Qka Mirza· International Conference on...· 0 citations
This research proposes the evaluation of a “proxy” DevSecOps pipeline, defined as an automated intermediary architecture that decouples intensive security scanning from the primary build flow to prevent bottlenecks and demonstrates that security validation is the most time-intensive part of the automated proxy workflow.
Abderrahim Rida, A. Bakhil, Ayoub Ait Lahcen· Future Internet· 0 citations