zxcvbn README

The project's README from github.com/dropbox/zxcvbn. As one markdown file: README.md.

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Build Status Sauce Test Status

zxcvbn is a password strength estimator inspired by password crackers. Through pattern matching and conservative estimation, it recognizes and weighs 30k common passwords, common names and surnames according to US census data, popular English words from Wikipedia and US television and movies, and other common patterns like dates, repeats (aaa), sequences (abcd), keyboard patterns (qwertyuiop), and l33t speak.

Consider using zxcvbn as an algorithmic alternative to password composition policy — it is more secure, flexible, and usable when sites require a minimal complexity score in place of annoying rules like "passwords must contain three of {lower, upper, numbers, symbols}".

For further detail and motivation, please refer to the USENIX Security '16 paper and presentation.

At Dropbox we use zxcvbn (Release notes) on our web, desktop, iOS and Android clients. If JavaScript doesn't work for you, others have graciously ported the library to these languages:

Integrations with other frameworks:

Installation

zxcvbn detects and supports CommonJS (node, browserify) and AMD (RequireJS). In the absence of those, it adds a single function zxcvbn() to the global namespace.

Bower

Install node and bower if you haven't already.

Get zxcvbn:

cd /path/to/project/root
bower install zxcvbn

Add this script to your index.html:

<script src="bower_components/zxcvbn/dist/zxcvbn.js">
</script>

To make sure it loaded properly, open in a browser and type zxcvbn('Tr0ub4dour&3') into the console.

To pull in updates and bug fixes:

bower update zxcvbn

Node / npm / MeteorJS

zxcvbn works identically on the server.

$ npm install zxcvbn
$ node
> var zxcvbn = require('zxcvbn');
> zxcvbn('Tr0ub4dour&3');

RequireJS

Add zxcvbn.js to your project (using bower, npm or direct download) and import as usual:

requirejs(["relpath/to/zxcvbn"], function (zxcvbn) {
    console.log(zxcvbn('Tr0ub4dour&3'));
});

Browserify / Webpack

If you're using npm and have require('zxcvbn') somewhere in your code, browserify and webpack should just work.

$ npm install zxcvbn
$ echo "console.log(require('zxcvbn'))" > mymodule.js
$ browserify mymodule.js > browserify_bundle.js
$ webpack mymodule.js webpack_bundle.js

But we recommend against bundling zxcvbn via tools like browserify and webpack, for three reasons:

See the performance section below for tips on loading zxcvbn stand-alone.

Tangentially, if you want to build your own standalone, consider tweaking the browserify pipeline used to generate dist/zxcvbn.js:

$ browserify --debug --standalone zxcvbn \
    -t coffeeify --extension='.coffee' \
    -t uglifyify \
    src/main.coffee | exorcist dist/zxcvbn.js.map >| dist/zxcvbn.js

Manual installation

Download zxcvbn.js.

Add to your .html:

<script type="text/javascript" src="path/to/zxcvbn.js"></script>

Usage

try zxcvbn interactively to see these docs in action.

zxcvbn(password, user_inputs=[])

zxcvbn() takes one required argument, a password, and returns a result object with several properties:

result.guesses            # estimated guesses needed to crack password
result.guesses_log10      # order of magnitude of result.guesses

result.crack_times_seconds # dictionary of back-of-the-envelope crack time
                          # estimations, in seconds, based on a few scenarios:
{
  # online attack on a service that ratelimits password auth attempts.
  online_throttling_100_per_hour

  # online attack on a service that doesn't ratelimit,
  # or where an attacker has outsmarted ratelimiting.
  online_no_throttling_10_per_second

  # offline attack. assumes multiple attackers,
  # proper user-unique salting, and a slow hash function
  # w/ moderate work factor, such as bcrypt, scrypt, PBKDF2.
  offline_slow_hashing_1e4_per_second

  # offline attack with user-unique salting but a fast hash
  # function like SHA-1, SHA-256 or MD5. A wide range of
  # reasonable numbers anywhere from one billion - one trillion
  # guesses per second, depending on number of cores and machines.
  # ballparking at 10B/sec.
  offline_fast_hashing_1e10_per_second
}

result.crack_times_display # same keys as result.crack_times_seconds,
                           # with friendlier display string values:
                           # "less than a second", "3 hours", "centuries", etc.

result.score      # Integer from 0-4 (useful for implementing a strength bar)

  0 # too guessable: risky password. (guesses < 10^3)

  1 # very guessable: protection from throttled online attacks. (guesses < 10^6)

  2 # somewhat guessable: protection from unthrottled online attacks. (guesses < 10^8)

  3 # safely unguessable: moderate protection from offline slow-hash scenario. (guesses < 10^10)

  4 # very unguessable: strong protection from offline slow-hash scenario. (guesses >= 10^10)

result.feedback   # verbal feedback to help choose better passwords. set when score <= 2.

  result.feedback.warning     # explains what's wrong, eg. 'this is a top-10 common password'.
                              # not always set -- sometimes an empty string

  result.feedback.suggestions # a possibly-empty list of suggestions to help choose a less
                              # guessable password. eg. 'Add another word or two'

result.sequence   # the list of patterns that zxcvbn based the
                  # guess calculation on.

result.calc_time  # how long it took zxcvbn to calculate an answer,
                  # in milliseconds.

The optional user_inputs argument is an array of strings that zxcvbn will treat as an extra 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 a user's personal information can be heavily penalized. This list is also good for site-specific vocabulary — Acme Brick Co. might want to include ['acme', 'brick', 'acmebrick', etc].

Performance

runtime latency

zxcvbn operates below human perception of delay for most input: ~5-20ms for ~25 char passwords on modern browsers/CPUs, ~100ms for passwords around 100 characters. To bound runtime latency for really long passwords, consider sending zxcvbn() only the first 100 characters or so of user input.

script load latency

zxcvbn.js bundled and minified is about 400kB gzipped or 820kB uncompressed, most of which is dictionaries. Consider these tips if you're noticing page load latency on your site.

Then try one of these alternatives:

  1. Put your <script src="zxcvbn.js"> tag at the end of your html, just before the closing </body> tag. This ensures your page loads and renders before the browser fetches and loads zxcvbn.js. The downside with this approach is zxcvbn() becomes available later than had it been included in <head> — not an issue on most signup pages where users are filling out other fields first.

  2. If you're using RequireJS, try loading zxcvbn.js separately from your main bundle. Something to watch out for: if zxcvbn.js is required inside a keyboard handler waiting for user input, the entire script might be loaded only after the user presses their first key, creating nasty latency. Avoid this by calling your handler once upon page load, independent of user input, such that the requirejs() call runs earlier.

  3. Use the HTML5 async script attribute. Downside: doesn't work in IE7-9 or Opera Mini.

  4. Include an inline <script> in <head> that asynchronously loads zxcvbn.js in the background. Advantage over (3): it works in older browsers.

// cross-browser asynchronous script loading for zxcvbn.
// adapted from http://friendlybit.com/js/lazy-loading-asyncronous-javascript/

(function() {

  var ZXCVBN_SRC = 'path/to/zxcvbn.js';

  var async_load = function() {
    var first, s;
    s = document.createElement('script');
    s.src = ZXCVBN_SRC;
    s.type = 'text/javascript';
    s.async = true;
    first = document.getElementsByTagName('script')[0];
    return first.parentNode.insertBefore(s, first);
  };

  if (window.attachEvent != null) {
    window.attachEvent('onload', async_load);
  } else {
    window.addEventListener('load', async_load, false);
  }

}).call(this);

Development

Bug reports and pull requests welcome!

git clone https://github.com/dropbox/zxcvbn.git

zxcvbn is built with CoffeeScript, browserify, and uglify-js. CoffeeScript source lives in src, which gets compiled, bundled and minified into dist/zxcvbn.js.

npm run build    # builds dist/zxcvbn.js
npm run watch    # same, but quickly rebuilds as changes are made in src.

For debugging, both build and watch output an external source map dist/zxcvbn.js.map that points back to the original CoffeeScript code.

Two source files, adjacency_graphs.coffee and frequency_lists.coffee, are generated by python scripts in data-scripts that read raw data from the data directory.

For node developers, in addition to dist, the zxcvbn npm module includes a lib directory (hidden from git) that includes one compiled .js and .js.map file for every .coffee in src. See prepublish in package.json to learn more.

Acknowledgments

Dropbox for supporting open source!

Mark Burnett for releasing his 10M password corpus and for his 2005 book, Perfect Passwords: Selection, Protection, Authentication.

Wiktionary contributors for building a frequency list of English words as used in television and movies.

Researchers at Concordia University for studying password estimation rigorously and recommending zxcvbn.

And xkcd for the inspiration :+1::horse::battery::heart:

zxcvbn (dropbox/zxcvbn)

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16.1K ·GitHub
这是什么?zxcvbn 是 Dropbox 开源的 JavaScript 密码强度评估工具,设计思路借鉴了密码破解器。它不是检查规则,而是通过模式匹配来评估一个密码有多容易被猜到,并给出简短的文字提示。
它是嵌到我网站里的库,还是我调用的 API?是一个库,不是服务:同一份脚本既能在浏览器里运行,也能通过 Node 在服务器上以完全相同的方式运行。在没有模块系统的环境下,它只是暴露一个全局函数 zxcvbn()。
怎么把它加到项目里?用 npm 或 Bower 安装,或者直接下载 zxcvbn.js 再加一个 script 标签。它会自动识别 CommonJS 和 AMD,所以在 Node、Meteor、RequireJS、browserify 或 webpack 环境里,require('zxcvbn') 开箱即用。
上线前有什么要注意的坑?体积:压缩并 gzip 之后仍有几百 KB(约 400 kB,大部分是词典),而且 README 建议不要用 browserify 或 webpack 打包它——更好的做法是在注册和重置密码这两个页面单独异步加载。
它是经过生产环境验证的,还是实验品?经过验证:Dropbox 自己就在网页、桌面、iOS 和 Android 客户端上使用 zxcvbn,康考迪亚大学的研究人员也对其评估结果做过严谨的研究。
为什么用 zxcvbn,而不是常见的“4 类字符至少满足 3 类”这种规则?README 把它定位成密码组合策略的算法化替代方案:固定规则既会放过 P@ssword1 这样的弱密码,又会拒掉强口令短语,而 zxcvbn 衡量的是真实的可猜测程度——所以网站可以要求达到最低的复杂度评分,而不是按字符类别定规则。
它能识别哪些弱密码模式?3 万个常见密码、美国人口普查中的名字和姓氏、来自维基百科以及美国影视的热门英文单词,还有日期、重复(aaa)、顺序(abcd)、键盘排列(qwertyuiop)和 l33t 写法。
它会向用户解释密码为什么弱吗?会。除了评分之外,它还会给出简洁、有针对性的文字提示,引导用户设置更难猜的密码——正是这一点让无需规则的简单注册表单变得可行。
我的后端是 Python 或 Go,那边也能用吗?可以。社区维护了 Python、C/C++、Rust、Go、Java、Ruby、C#、Objective-C、Scala、PHP 和 OCaml 的移植版本,还有一个 REST API(zxcvbn-api),不用 JavaScript 也能在服务器端评分。
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