Automated Vulnerability Assessment and Penetration Testing Tool for CCTV Cameras and DVR

Each CCTV camera and DVR is an ideal target for cyber attacks due to a growing concern regarding insecure configuration, firmware, and network services offered by the deployment of IP-based surveillance systems. Manual VAPT is time-consuming and requires specialized skills. This paper describes the development of an automated VAPT framework that can identify, analyze, and report security vulnerabilities in CCTV and DVR systems. The automated framework integrates network scanning, service fingerprinting, and vulnerability mapping with exploit test validation via automated modules using Python scripting and open source security engines such as Nmap, Nikto, or Metasploit. The system has the ability to perform end-toend security testing, credential testing, open port enumeration, default password tests, and examine firmware for vulnerabilities. Real-time test results can be visualized in a central web-based dashboard indicating severity and remediation recommendations. The framework was experimentally validated across multiple CCTV brands reporting an 87% efficacy rate identifying critical risks, and a 65% reduction in manual testing time. This enhances operational efficiency, scalability, and reliability of VAPT practices; thus providing an effective security solution to public and private surveillance infrastructures threatened by modern cyber attacks.