zxcvbn-python

A realistic password strength estimator.

This is a Python implementation of the library created by the team at Dropbox. The original library, written for JavaScript, can be found here.

While there may be other Python ports available, this one is the most up to date and is recommended by the original developers of zxcvbn at this time.

Features

• Tested in Python versions 2.6-2.7, 3.3-3.6
• Accepts user data to be added to the dictionaries that are tested against (name, birthdate, etc)
• Gives a score to the password, from 0 (terrible) to 4 (great)
• Provides feedback on the password and ways to improve it
• Returns time estimates on how long it would take to guess the password in different situations

Installation

Install the package using pip: pip install zxcvbn-python

Usage

Pass a password as the first parameter, and a list of user-provided inputs as the user_inputs parameter (optional).

from zxcvbn import zxcvbn

results = zxcvbn('JohnSmith123', user_inputs=['John', 'Smith'])

print(results)

Output:

{
    'password': 'JohnSmith123', 
    'score': 2, 
    'guesses': 2567800, 
    'guesses_log10': 6.409561194521849, 
    'calc_time': datetime.timedelta(0, 0, 5204)
    'feedback': {
        'warning': '', 
        'suggestions': [
            'Add another word or two. Uncommon words are better.', 
            "Capitalization doesn't help very much"
        ]
    }, 
    'crack_times_display': {
        'offline_fast_hashing_1e10_per_second': 'less than a second'
        'offline_slow_hashing_1e4_per_second': '4 minutes', 
        'online_no_throttling_10_per_second': '3 days', 
        'online_throttling_100_per_hour': '3 years', 
    }, 
    'crack_times_seconds': {
        'offline_fast_hashing_1e10_per_second': 0.00025678, 
        'offline_slow_hashing_1e4_per_second': 256.78
        'online_no_throttling_10_per_second': 256780.0, 
        'online_throttling_100_per_hour': 92440800.0, 
    }, 
    'sequence': [{
        'matched_word': 'john', 
        'rank': 2, 
        'pattern': 'dictionary', 
        'reversed': False, 
        'token': 'John', 
        'l33t': False, 
        'uppercase_variations': 2, 
        'i': 0, 
        'guesses': 50, 
        'l33t_variations': 1, 
        'dictionary_name': 'male_names', 
        'base_guesses': 2, 
        'guesses_log10': 1.6989700043360185, 
        'j': 3
    }, {
        'matched_word': 'smith123', 
        'rank': 12789, 
        'pattern': 'dictionary', 
        'reversed': False, 
        'token': 'Smith123', 
        'l33t': False, 
        'uppercase_variations': 2, 
        'i': 4, 
        'guesses': 25578, 
        'l33t_variations': 1, 
        'dictionary_name': 'passwords', 
        'base_guesses': 12789, 
        'guesses_log10': 4.407866583030775, 
        'j': 11
    }], 
}

Custom Ranked Dictionaries

In order to support more languages or just add password dictionaries of your own, there is a helper function you may use.

from zxcvbn.matching import add_frequency_lists

add_frequency_lists({
    'my_list': ['foo', 'bar'],
    'another_list': ['baz']
})

These lists will be added to the current ones, but you can also overwrite the current ones if you wish. The lists you add should be in order of how common the word is used with the most common words appearing first.

Contribute

• Report an Issue: https://github.com/dwolfhub/zxcvbn-python/issues
• Submit a Pull Request: https://github.com/dwolfhub/zxcvbn-python/pulls

License

The project is licensed under the MIT license.