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@article{195813,
        author = {Kandoor Deekshitha and Khatha Mythily and Malapati Geethika and Kavya Kancharla and Mukesh Gilda},
        title = {Automated Penetration Testing Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1042-1048},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195813},
        abstract = {Web applications are increasingly targeted by cyberattacks due to vulnerabilities such as SQL Injection (SQLi), Cross-Site Scripting (XSS), and Cross-Site Request Forgery (CSRF), which are among the most critical risks identified in modern web security. Traditional penetration testing methods are often manual, time-consuming, and require expert knowledge, making them less accessible for small-scale developers and organizations.
This paper proposes an Automated Penetration Testing Framework designed to efficiently identify common web application vulnerabilities. The proposed system integrates a modular scanning engine that performs automated payload injection and response analysis to detect security flaws. The framework is implemented using Python and incorporates technologies such as Flask for backend processing, along with web crawling and parsing techniques for dynamic content analysis.The system evaluates application responses using error detection, payload reflection, and form analysis mechanisms to accurately identify vulnerabilities. Detected issues are categorized based on severity levels and presented through a user-friendly web interface. Additionally, the framework generates comprehensive reports in multiple formats, including HTML, JSON, and CSV, enabling easy interpretation and integration with other tools.
Experimental results demonstrate that the proposed framework effectively detects common vulnerabilities and reduces the effort required for manual testing. The system provides a cost-effective and scalable solution for improving web application security. Future enhancements include support for advanced attack detection, machine learning- based analysis, and cloud-based deployment.},
        keywords = {Automated Penetration Testing, Web Application Security, SQL Injection (SQLi), Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Vulnerability Scanning, Cybersecurity, Flask, Python, Security Testing.},
        month = {April},
        }