nbvcxz is java library (and standalone console program) which is heavily inspired by the work in zxcvbn.
Password strength estimation is a bit of an art and science. Strength estimation is accomplished by running
a password through different algorithms looking for matches in any part of the password on: word lists (with fuzzy matching),
common dates, common years, spacial patterns, repeating characters, repeating sets of characters,
and alphabetic sequences.
Each of these represent ways an attacker may try to crack a password. To be vigilant, we must adapt to new methods in password cracking and implement new methods to identify passwords susceptible to each new method.
- Maven Central
- Compile
- Differentiating Features
- Compatibility
- A Rant On Arbitrary Password Policies
- How to use
- Bugs and Feedback
- License
- Requires Java
- Application using this library
<dependency>
<groupId>me.gosimple</groupId>
<artifactId>nbvcxz</artifactId>
<version>1.5.1</version>
</dependency>apt-get install git
apt-get install openjdk-8-jdk
apt-get install maven
git clone https://github.com/GoSimpleLLC/nbvcxz.git
cd nbvcxz
mvn packageThe project will be built, and the jar file will be placed in the target sub-directory.
- Internationalization support for all text output by the library (for feedback, console output, etc).
- Currently supported languages
- English (default)
- Afrikaans (af)
- Dutch (nl)
- Finnish (fi)
- French (fr)
- German (de)
- Hungarian (hu)
- Italian (it)
- Portuguese (pt)
- Russian (ru)
- Spanish (es)
- Swedish (sv)
- Telugu (te)
- Ukrainian (uk)
- Chinese (zh)
- Currently supported languages
- Better match generation algorithm which will find the absolute lowest entropy combination of the matches.
- Support for ranked and un-ranked dictionaries.
- Dictionary matching has the ability to use Levenshtein Distance (LD) calculations to match passwords which are non-exact matches to a dictionary entry.
- LD calculations happen on full passwords only, and have a
thresholdof 1/4th the length of the password.
- LD calculations happen on full passwords only, and have a
- Dictionaries can be customized, and custom dictionaries can be added very easily.
- Exclusion dictionaries can also be built and tailored per-user to prevent obvious issues like using their own email or name as their password
- Default dictionaries have excluded single character words due to many false positives
- Additional PasswordMatchers and Matches can be implemented and configured to run without re-compiling.
- Easy to configure how this library works through the ConfigurationBuilder.
- You can set minimum entropy scores, locale, year patterns, custom leet tables, custom adjacency graphs, custom dictionaries, and custom password matchers.
- Support for generating passwords and passphrases.
- Available in the console application as well as the library.
- One use case is for generating a "forgot password" temporary pass
Strict compatibility between nbvcxz and zxcvbn has not been a goal of this project. The additional features in nbvcxz which have improved accuracy are the main causes for differences with zxcvbn. There are some ways to configure nbvcxz for better compatibility though, so we will go over those configuration parameters here.
-
Disable the Levenshtein Distance (LD) calculation. This feature was very helpful in my analysis on helping identify passwords which were only slightly different than dictionary words but were not caught with the original implementation. This feature will be sure to cause nbvcxz to produce different results than zxcvbn for a large number of passwords. Use ConfigurationBuilder setDistanceCalc(Boolean distanceCalc)
-
Make sure both implementations are using the same dictionaries. There are additional leaked passwords in the nbvcxz dictionary compared to zxcvbn. There are also additional dictionaries included in nbvcxz that are not in zxcvbn and vice versa. Simply different choices on what lists were important to include by default. With nbvcxz you can easily change which dictionaries are used though, so it's easy to make the different implementations use the same dictionaries. Use ConfigurationBuilder setDictionaries(List dictionaries)
-
Disable separator match types. This is a new match type which zxcvbn has no equivalent. It helps with passphrase detection and accurately scoring them, but if we are going for compatibility we need to disable it. Use ConfigurationBuilder setPasswordMatchers(List passwordMatchers)
-
The algorithm to find the best matches is different between nbvcxz and zxcvbn, that is likely to produce slightly different results in cases where zxcvbn is unable to find the best combination of matches due to the algorithm used. There were quite a few instances I noted that brought about the change to the algorithm used by nbvcxz where there were obviously "wrong" results for entropy based on the combination of matches because it got stuck in a local minimum. This is no longer an issue with nbvcxz, but will inherently produce different results for some passwords compared to the original algorithm used by zxcvbn. In the majority of cases both algorithms are able to figure out what the lowest entropy combination of matches on the password are, so I don't see this being too big of an issue.
Lets think up an example scenario which I expect some of you may have run into way too often.
We are a company NewStartup! and we are creating the next big web application. We
want to ensure our users don't choose an easily guessable password, so we implement an arbitrary
policy which says a password must have:
an eight character minimum and contain upper case, lower case, numbers, and special characters
Now lets see how that policy applies to two passwords which are at opposite ends of the spectrum.
Password #1: Passw0rd! - This password was chosen to get around an arbitrary policy
Password #2: 5fa83b7e1r39xfa8hmiz0 - This was randomly generated using lowercase alphanumeric
Password #1 meets all of the rules in the policy and passes with flying colors. Password #2 does not contain upper case, or special characters, and thus the policy fails this password.
Was password #1 actually more secure than password #2 by any metric? That would be a hard argument to make.
In fact, password #1 is likely to be cracked quite quickly. password is one of the top passwords in all password
lists an attacker is likely to try using a rule based dictionary attack. If the attacker knows that our policy requires:
eight character minimum, upper case, lower case, numbers, and special characters
they will then use rules like toggle case, l33t substitution, and suffix/prefix special characters
to augment their dictionary list for the attack.
It's quite likely password #1 would fall to an attacker even in a rate limited online attack.
Password #2, while not allowed by our policy, is only susceptible to a brute force attack (if a secure hashing algorithm is used).
nbvcxz can be used as a stand-alone console program, or import it as a library.
To use as a stand-alone program, just compile, and run it by calling:
java -jar nbvcxz-1.5.1.jar
nbvcxz can also be used as a library for password validation in java back-ends.
Below is a full example of the pieces you'd need to implement within your own application.
// With all defaults...
Nbvcxz nbvcxz = new Nbvcxz();Here we're creating a custom configuration which localizes all text to French
// Create our configuration object and set the locale
Configuration configuration = new ConfigurationBuilder()
.setLocale(Locale.forLanguageTag("fr"))
.createConfiguration();
// Create our Nbvcxz object with the configuration we built
Nbvcxz nbvcxz = new Nbvcxz(configuration);Here we're creating a custom configuration with a custom exclusion dictionary and minimum entropy
// Create a map of excluded words on a per-user basis using a hypothetical "User" object that contains this info
List<Dictionary> dictionaryList = ConfigurationBuilder.getDefaultDictionaries();
dictionaryList.add(new DictionaryBuilder()
.setDictionaryName("exclude")
.setExclusion(true)
.addWord(user.getFirstName(), 0)
.addWord(user.getLastName(), 0)
.addWord(user.getEmail(), 0)
.createDictionary());
// Create our configuration object and set our custom minimum
// entropy, and custom dictionary list
Configuration configuration = new ConfigurationBuilder()
.setMinimumEntropy(40d)
.setDictionaries(dictionaryList)
.createConfiguration();
// Create our Nbvcxz object with the configuration we built
Nbvcxz nbvcxz = new Nbvcxz(configuration);// Estimate password
Result result = nbvcxz.estimate(password);
return result.isMinimumEntropyMet();This part will need to be integrated into your specific front end, and really depends on your needs. Here are some of the possibilities:
// Get formatted values for time to crack based on the values we
// input in our configuration (we used default values in this example)
String timeToCrackOff = TimeEstimate.getTimeToCrackFormatted(result, "OFFLINE_BCRYPT_12");
String timeToCrackOn = TimeEstimate.getTimeToCrackFormatted(result, "ONLINE_THROTTLED");
// Check if the password met the minimum set within the configuration
if(result.isMinimumEntropyMet())
{
// Start building success message
StringBuilder successMessage = new StringBuilder();
successMessage.append("Password has met the minimum strength requirements.");
successMessage.append("<br>Time to crack - online: ").append(timeToCrackOn);
successMessage.append("<br>Time to crack - offline: ").append(timeToCrackOff);
// Example "success message" that would be displayed to the user
// This is obviously just a contrived example and would have to
// be tailored to each front-end
setSuccessMessage(successMessage.toString());
return true;
}
else
{
// Get the feedback for the result
// This contains hints for the user on how to improve their password
// It is localized based on locale set in configuration
Feedback feedback = result.getFeedback();
// Start building error message
StringBuilder errorMessage = new StringBuilder();
errorMessage.append("Password does not meet the minimum strength requirements.");
errorMessage.append("<br>Time to crack - online: ").append(timeToCrackOn);
errorMessage.append("<br>Time to crack - offline: ").append(timeToCrackOff);
if(feedback != null)
{
if (feedback.getWarning() != null)
errorMessage.append("<br>Warning: ").append(feedback.getWarning());
for (String suggestion : feedback.getSuggestion())
{
errorMessage.append("<br>Suggestion: ").append(suggestion);
}
}
// Example "error message" that would be displayed to the user
// This is obviously just a contrived example and would have to
// be tailored to each front-end
setErrorMessage(errorMessage.toString());
return false;
}We have a passphrase/password generator as part of nbvcxz which very easy to use.
// Generate a passphrase from the standard (eff_large) dictionary with 5 words with a "-" between the words
String pass1 = Generator.generatePassphrase("-", 5);
// Generate a passphrase from a custom dictionary with 5 words with a "-" between the words
String pass2 = Generator.generatePassphrase(new Dictionary(...), "-", 5);// Generate a random password with alphanumeric characters that is 15 characters long
String pass = Generator.generateRandomPassword(Generator.CharacterTypes.ALPHANUMERIC, 15);For bugs, questions and discussions please use the Github Issues.
MIT License
- Java 1.7+
- Blacksmith TPM - Formerly GoSimple TPM
- Pazzword - Intelligent Password Evaluator
- KeePassDX - Open source password manager for Android
Anyone else using the library in their application, i'd love to hear and put a link up here.