THIS VERSION IS DEPRECATED

See https://github.com/dwolfhub/zxcvbn-python for a maintained version.

(The original README follows.)

This is a python port of zxcvbn, which is a JavaScript password strength generator. zxcvbn attempts to give sound password advice through pattern matching and conservative entropy calculations. It finds 10k common passwords, common American names and surnames, common English words, and common patterns like dates, repeats (aaa), sequences (abcd), and QWERTY patterns.

Please refer to http://tech.dropbox.com/?p=165 for the full details and motivation behind zxcbvn. The source code for the original JavaScript (well, actually CoffeeScript) implementation can be found at:

https://github.com/lowe/zxcvbn

For full motivation, see:

http://tech.dropbox.com/?p=165

Use

The zxcvbn module exports the password_strength() function. Import zxcvbn, and call password_strength(password, user_inputs=[]). The function will return a result dictionary with the following keys:

entropy # bits

crack_time # estimation of actual crack time, in seconds.

crack_time_display # same crack time, as a friendlier string:

"instant", "6 minutes", "centuries", etc.

score # [0,1,2,3,4] if crack time is less than

[102, 104, 106, 108, Infinity].

               # (useful for implementing a strength bar.)

match_sequence # the list of patterns that zxcvbn based the

entropy calculation on.

calculation_time # how long it took to calculate an answer,

in milliseconds. usually only a few ms.

The optional user_inputs argument is an array of strings that zxcvbn will add to its internal dictionary. This can be whatever list of strings you like, but is meant for user inputs from other fields of the form, like name and email. That way a password that includes the user's personal info can be heavily penalized. This list is also good for site-specific vocabulary.

Bug reports and pull requests welcome!

Acknowledgments

Dropbox, thank you again for supporting independent projects both inside and outside of hackweek.

Thanks to Dan Wheeler (https://github.com/lowe) for the CoffeeScript implementation (see above.) To repeat his outside acknowledgements (which remain useful, as always):

Many thanks to Mark Burnett for releasing his 10k top passwords list: http://xato.net/passwords/more-top-worst-passwords and for his 2006 book, "Perfect Passwords: Selection, Protection, Authentication"

Huge thanks to Wiktionary contributors for building a frequency list of English as used in television and movies: http://en.wiktionary.org/wiki/Wiktionary:Frequency_lists

Last but not least, big thanks to xkcd :) https://xkcd.com/936/