PassCheck

A cross-platform CLI tool for password security: breach checking via the HIBP API, secure password generation, and ML-powered strength scoring with specific suggestions.

Quick Install

git clone https://github.com/NameRectified/passcheck.git
cd passcheck
pip install -e .

Then run passcheck anywhere.

Usage

Interactive mode

passcheck

Shows a menu where you can pick Check or Generate without remembering flags.

Check a password

passcheck check
Password: ********

Breach Status: Found in 2,266,543 data breaches
Strength:       25/100 (weak)

Suggestions:
  • This password appears in 2,266,543 data breach(es) — never reuse it
  • Add at least one uppercase letter
  • Add at least one special character
  • Avoid sequential characters like '123' or 'abc'
  • Use a mix of upper/lowercase, digits, and special characters

Suggested replacement: :_Y8$4Ur/v-&6FuN (copied to clipboard)

You can also pass the password directly:

passcheck check yourpassword

Omitting the argument prompts hidden-ly with getpass.

Generate a password

passcheck generate -l 16

Generated:      G_j2*8TUP8?7
Strength:       65/100 (strong)
Copied to clipboard.

• -l, --length: Password length (default: 16)

Features

• Breach checking: Uses the Have I Been Pwned API with k-anonymity — never sends the full hash over the network.
• Password generation: Uses secrets module. Excludes ambiguous characters (0, O, 1, l, I, |). Guarantees at least one uppercase, one lowercase, three digits, and one special character.
• ML strength scoring: RandomForest model trained on 3500 passwords. Scores 0–100 with specific suggestions for improvement.
• Specific suggestions: Suggestions derived from the actual password — not generic templates.

Requirements

• Python 3.10+
• requests, pyperclip, scikit-learn, joblib

Project Structure

passcheck/
├── passcheck.py     CLI entry point
├── checker.py       Breach checking (HIBP API)
├── generator.py     Password generation
├── scorer.py        ML strength analysis + suggestions
├── config.py        Configuration constants
├── training/        ML pipeline (features, dataset, training)
├── tests/           pytest test suite (32 tests)
├── models/          Trained model (.pkl)
├── .progress/       Session log and interview prep
├── requirements.txt
└── pyproject.toml

Running Tests

pytest tests/

License

MIT