Password hashing as a microservice with Docker Compose
So you’ve stopped breaking the law by storing your passwords in plain text, and you’re aware that MD5 doesn’t cut it any more in a world of GPU-based cracking tools, so you’ve started using bcrypt instead. But can you make your passwords any more secure? Can you even protect your users who have chosen passwords such as “password” or “qwerty” or “123456” or “letmein”? And since bcrypt is so computationally expensive, how can you stop an attempt to brute force your admin password from bringing your entire site down?
Here’s what you can do. Use a microservice.
This gives you two advantages:
- Your passwords are stored in a separate database from your user accounts. The “password” field in your users table only contains a randomly allocated identifier; without the password database as well, this tells an attacker nothing. Nada. Nichts. Bupkis.
- Your password hash algorithm can be scaled independently from the rest of your site. It can be run on completely different hardware. A brute force attack on your admin site won’t end up DOS-ing everything else, no matter how slow you make it.
Over the past week or so I’ve been experimenting with an implementation of this approach. You can find it on GitHub here.
How it works
Since it’s 2016 and Docker is all the rage, the password hashing microservice, the main web application, and their respective databases, are all in separate Docker containers, managed as a group using Docker Compose and defined in the
docker-compose.yml file. Tools such as Docker Swarm or Kubernetes make it a breeze to scale up and down as needed.
The password hashing microservice is in
/passwords in the Git repo. It is implemented as a very basic Flask application in passwords/serve.py, storing the passwords in a MongoDB database. It exposes three methods:
POST /password: hash a password, allocate it a random ID, and save it.
POST /password/test/<id>: test a password against the saved hash with the given ID.
DELETE /password/<id>: delete the hash with the given ID from the database.
The POST methods both take the password in a form field called
password. The password ID returned by the first method is a randomly generated GUID.
The sample web application is a Django application in
/web in the Git repo, backed by a Postgresql database. The file web/webapp/security.py contains a custom password hasher which saves the GUID returned from the microservice in the password column in the users table. The password ID thereby acts as a proxy for the password hash, but since it can not be derived from the hash, the password can not be brute forced from it.
docker-compose.yml file also puts a Træfɪk proxy server between the web application and the microservice to act as a load balancer. This allows you to scale the microservice as necessary simply by typing
docker-compose scale passwords-svc=<n> where
<n> is the number of containers you wish to spin up.
Ramping up the security even further…
To make things even harder for an attacker, the hashing service combines both the password and its identifier with application-specific secret strings before saving them into the password database. These secrets will frustrate anyone who manages to get hold of both the user database and the password database, as unless you know their values, you will not be able to brute force the passwords, nor will you be able to tell which password hashes correspond to which users in the database.
These secrets are stored as environment variables called
KEY_SECRET and are defined in this example in the
docker-compose.yml file. Naturally, you should change them in your own application to other, similarly complex, random strings. If possible, you should load them in from a dedicated secret store such as Hashicorp Vault.
Unless the attacker has access to the user database and the password database and both these secret keys, they aren’t going to be able to crack any of your users’ passwords. Even if your users have used silly passwords such as “password” or “123456” or “arsenal” or “chelsea” their credentials will still be safe.
This is what microservices are for.
There’s a lot of debate going on at the moment about how to use microservices. Should you keep your application as a single monolith and only extract out certain tiny pieces of functionality, or should you break it down into a large number of separate microservices? In each case, just how much should each microservice handle?
Password hashing, as we’ve implemented it here, is a great example of where microservices are appropriate, and how much they should try to achieve. We’ve identified some very specific problems. There is no speculation, guesswork or YAGNI involved. Most importantly, we’ve seen that they provide well defined real-world benefits.