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im just gunna scan a bunch of CFD sims and look for weird trends then maybe take that to the lab to see if I was wrong. maybe write a math paper on it or somthing. then prolly just take all that data and feed it a machine learning model, but im getting WAY a head of myself here. im still working on forking properly.

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Open Source Python Security Scanner

Overview

This project is a Python-based security scanner that works with both GitHub repositories and local directories. Currently, it scans only Python files for vulnerabilities, using Bandit for code analysis and checking for exposed secrets in configuration files.

Features

  • Clone GitHub Repositories: Automatically clone a GitHub repository to scan it.
  • Code Scanning: Uses Bandit to find security issues in Python code.
  • Configuration Scanning: Detects exposed secrets in .env files.
  • Reports: Generates detailed reports in the reports/ directory.

Usage

Cloning and Scanning a GitHub Repository

python3 cmd/main.py https://github.com/user/repo.git

Scanning a Local Directory

python3 cmd/main.py /path/to/local/directory

Example with a Vulnerable Flask Project

To see the scanner in action, you can use a deliberately vulnerable Flask project. From the project root, run the scanner with the following command:

python3 cmd/main.py https://github.com/we45/Vulnerable-Flask-App.git

This example uses Vulnerable-Flask-App which is an intentionally vulnerable Flask application. Running the command will scan the project for vulnerabilities and generate reports in the reports/ directory.

Requirements

Install the required Python packages with:

pip install -r requirements.txt

Output

  • Vulnerability Scan Report: vulnerability_scan_report.txt
  • Final Security Report: security_report.json

Contributing

Feel free to fork this repository and submit pull requests to improve the scanner.

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im just gunna scan a bunch of CFD sims and look for weird trends then maybe take that to the lab to see if I was wrong. maybe write a math paper on it or somthing. then prolly just take all that data and feed it a machine learning model, but im getting WAY a head of myself here. im still working on forking properly.

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