Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{195522,
        author = {Jagannath Mohanty and Nikhil Ku Gupta and Sonu and Priyansh Pal},
        title = {AUTO: Automated Domain Reconnaissance and Vulnerability Assessment Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {981-984},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195522},
        abstract = {The exponential growth of web applications, cloud-based services, and distributed enterprise infrastructures has significantly expanded the digital attack surface of organizations worldwide. As businesses increasingly rely on online platforms, domain ecosystems now consist of numerous subdomains, APIs, third-party integrations, and dynamically generated resources. This complexity introduces critical security challenges, making reconnaissance a fundamental phase in penetration testing and vulnerability assessment.
Traditional reconnaissance methodologies depend on multiple independent open-source tools for subdomain discovery, port scanning, vulnerability detection, and data collection. However, these tools often operate in isolation, requiring manual coordination, repetitive execution, and fragmented result analysis, which can lead to inefficiencies, inconsistent reporting, and overlooked security risks.
This paper presents AUTO (Automated Domain Reconnaissance Tool), an integrated and automated framework designed to streamline the entire reconnaissance lifecycle within a unified and structured environment. AUTO combines passive and active subdomain enumeration, historical URL extraction, live host detection, parameter discovery, JavaScript analysis, high-speed port scanning, and vulnerability assessment for XXE, SSRF, and CORS misconfigurations.
The framework integrates multiple open-source utilities into a sequential workflow pipeline that automates data aggregation, validation, and structured reporting. Additionally, AUTO incorporates visual reconnaissance through automated screenshot capture to enhance attack surface visibility.
By consolidating diverse reconnaissance processes into a single automation framework, AUTO reduces manual effort, minimizes human error, improves operational efficiency, and enhances coverage depth. Overall, AUTO transforms traditional reconnaissance into a scalable and intelligent cybersecurity solution.},
        keywords = {Domain Reconnaissance, Subdomain Enumeration, Vulnerability Assessment, Cybersecurity Automation, XXE, SSRF, CORS, Port Scanning, Open-Source Intelligence (OSINT), Security Testing.},
        month = {April},
        }