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Serverless Pwned Passwords

Serverless Pwned Passwords uses serverless functions to provide an API for checking potential passwords against an enormous corpus of passwords leaked from security breaches.

Bloom filters generated from the corpus of leaked passwords are used to test for membership. Go functions running on Apache OpenWhisk expose an API to check passwords against the list.

💪 Testing a potential password against 320 million leaked passwords takes milliseconds! 💪

Serverless Bloom Filters

usage

Need an instance of Apache OpenWhisk to use? Sign up for IBM Bluemix which comes with a generous free tier.

Having installed the action using instructions below, test the service using the command-line.

bloom_filters takes a single parameter (password) with the potential password. found is returned as true if password test was positive.

Let's check the most commonly used password of all time…. password!

$ wsk action invoke bloom_filters --result --param password password
{
    "found": true
}

Unsurprisingly, this was found in the leaked password corpus.

What about something else?

$ wsk action invoke bloom_filters --result --param password serverless
{
    "found": false
}

Great, serverless was not found and I can keep using it… 😉

web actions

Exposing our service as a "web action" provides a public API endpoint for our serverless function.

$ wsk action update bloom_filters --web true
ok: updated action bloom_filters
$ wsk action get bloom_filters --url
ok: got action bloom_filters
https://openwhisk.host/api/v1/web/user@email/default/bloom_filters

Allowing us to call it using a normal HTTP request.

$ http get https://openwhisk.host/api/v1/web/user@email/default/bloom_filters.json?password=password
{
    "found": true
}

performance

Looking at the logging output shows performance data for each password check.

$  wsk activation logs 2818b2657a7043918cf4495b7b6083ff
2017-08-15T15:17:18.833465399Z stderr: checking password: password
2017-08-15T15:17:18.833497727Z stderr: hash: 5BAA61E4C9B93F3F0682250B6CF8331B7EE68FD8
2017-08-15T15:17:18.83350474Z  stderr: reading file @ /bloom_filters/5b.dat
2017-08-15T15:17:18.833509954Z stderr: file contained 2246528 bytes
2017-08-15T15:17:18.83351495Z  stderr: elapsed time: 1.780073ms
2017-08-15T15:17:18.833519804Z stderr: decoding bloom filter from 2246528 bytes
2017-08-15T15:17:18.833524969Z stderr: decoded bloom filter parameters -> m: 17971985 k: 10
2017-08-15T15:17:18.833530802Z stderr: decoding bloom filter took 5.910131ms
2017-08-15T15:17:18.833535692Z stderr: found password in bloom filter: true

installation

Follow the instructions below to create the bloom filters, build the runtime image and create the OpenWhisk action.

Building and publishing the action runtime image is an optional step. If you would rather skip this step, use this pre-existing Docker image: https://hub.docker.com/r/jamesthomas/bloom_filters/

download pwned passwords (optional)

There is a Homebrew Formula for installing the tool on OS X.

generate bloom filters (optional)

  • Install Bloom Filter library

    $ go get -u github.com/willf/bloom

  • Run Bloom Filter generator program (generate/generate_filters.go) with hash file arguments.

    $ cd generate
$ go run generate_filters.go pwned-passwords-1.0.txt pwned-passwords-update-1.txt pwned-passwords-update-2.txt 
2017/08/14 17:43:09 Creating Bloom filters with parameters --> n: 17971985 k: 10
2017/08/14 17:43:09 Initialising 256 bloom filters...
2017/08/14 17:43:09 Bloom filter buckets: 256
2017/08/14 17:43:09 Reading hashes from file:  pwned-passwords-update-1.txt
2017/08/14 17:43:10 Processed 1000000 hashes...
...
2017/08/14 17:43:19 Added XXX hashes to bloom filters
2017/08/14 17:43:19 Serialising 256 bloom filters...
2017/08/14 17:43:19 bucket: d1 encoded bytes: XXX
...

Generated bloom filters will be serialised to files in the bloom_filters directory. Files use the bucket identifier as the file name (00.dat -> ff.dat).

build runtime image (optional)

Hosting images on Docker Hub requires registering a (free) account @ https://hub.docker.com/

  • Build the Docker image for the runtime.

    $ docker build -t <DOCKERHUB_USER>/bloom_filters .

  • Push Docker image to Docker Hub.

    $ docker push <DOCKERHUB_USER>/bloom_filters

build openwhisk action

  • Build Go binary for OpenWhisk runtime.

    $ env GOOS=linux GOARCH=amd64 go build -o exec bloom_filters.go

  • Add binary to zip file.

    $ zip action.zip exec

create openwhisk action

  • Create OpenWhisk action using binary archive file and Docker image.

    $ wsk action create bloom_filters --docker <DOCKERHUB_USER>/bloom_filters action.zip

Once the action has been created, use the instructions above for testing it out.

customising

Bloom filters use two parameters to control the probability of false positives, the size of the bit field (m) and the number of hashing functions (k).

Given a desired false positive rate and the number of items to be stored, optimal values for these parameters can be calculated. Bloom filter calculators exist online: https://hur.st/bloomfilter

In this example, ~320 million passwords need to be checked with a false positive rate of 0.001%.

Optimal Bloom filter parameter values given these conditions are:

  • m: 4,600,828,022 (548.46MB)
  • k: 10

Instantiating a Bloom filter of this size would add unacceptable delays during "cold" invocations. Splitting the password hashes into groups and generating Bloom filters for each group can be used to reduce the Bloom filter size.

Password hashes are split into 256 buckets using the first two hex characters from the string. Each Bloom filter needs to match 1.25M password hashes rather than 320M.

Bloom filter parameter values given these new conditions are:

  • m: 17,971,985 (2.14MB).
  • k: 10

Parameters for m and k are stored in the generate.go file.