Because this tool processes sensitive server logs (Apache/Nginx) using pandas, regex, and ipaddress, we focus on:

• Regular Expression Denial of Service (ReDoS): We use the regex library to mitigate some risks, but complex patterns in log_parser.py must be audited for catastrophic backtracking.
• IP Address Validation: Using the ipaddress module to prevent injection attacks or SSRF-related vulnerabilities when resolving or filtering log sources.
• Data Leakage in Visualizations: Ensuring visualizer.py (via seaborn/matplotlib) does not inadvertently expose sensitive PII (Personally Identifiable Information) like full IP addresses in public reports if not intended.
• Large File Vulnerability: Protecting traffic_analyzer.py from memory exhaustion (OOM) when loading massive .log files into pandas DataFrames.

Do not report security vulnerabilities via public GitHub issues.

1. Private Report: Please use the GitHub Private Vulnerability Reporting feature.
2. Email: Alternatively, contact [security@mscbuild.dev].
3. Response: We aim to acknowledge all reports within 48 hours and provide a fix or mitigation within 14 days.

• Environment: Always use a virtual environment (python -m venv venv).
• Audit: Run pip-audit regularly to check pandas and numpy for known CVEs.
• Privacy: If sharing output/ files, ensure you have anonymized sensitive log data.