Penetration tests are designed to assess the security of systems, requiring testers to efficiently share information and document findings. A collaboration platform that utilizes machine learning is hypothesized to enhance this process by automating data collection and reporting. We evaluate computer vision for data collection and analysis of penetration testing tools, aiming to alleviate manual reporting burdens and improve the effectiveness in penetration testing teams. The proposed solution integrates computer vision, neural networks and large language models to understand and analyze outputs from various penetration testing tools without manual log parsing. By comparing different tools and methods, this study aims to streamline collaboration during penetration tests and automate the collection of actionable data for penetration testers.