NNN-CVE

Problem

Penetration tests routinely produce fragmented output across Nmap, Nikto, and Nessus. Turning three tools' worth of raw findings into one coherent, client-ready report is a manual step that eats hours per engagement.

Approach

Built a Streamlit application that ingests live scans or uploaded reports from all three tools, normalizes findings into a common schema, enriches them with likely CVEs using local CVE data and NLP similarity, and exports the result as PDF, CSV, JSON, or HTML deliverables with severity-distribution visualizations.

Stack

• Python
• Streamlit
• Pandas
• Plotly
• ReportLab
• python-nmap
• scikit-learn
• NLTK

Outcome

Collapses hours of manual report assembly into a single pass — from raw scanner output to a client-ready, CVE-enriched deliverable.