Docker Deployment Best Practices

Given: There’s a CI system that automatically builds docker images from your VCS (e.g. git), we use self-hosted gitlab.
Goal: Both initial and subsequent automated deployments to different environments (staging and production).

Rejected Approaches

Most existing blog articles and howtos for this use case, specifically in the context of gitlab, tend to be relatively simple, relatively easy, and very very wrong. The biggest issue is with root access to the production server. I believe that developers (and the CI/CD system) should not have full root access to the production system(s), to retain semblance of separation in case of breaches. Yes, sure, a malicious developer could still check-in bad code which might eventually get deployed to production, but there is (should be) a review process before that, and traces in the VCS.

And yet, most recommendations on how to do deployment with gitlab circle around one of two approaches:

  1. Install a gitlab “runner” on your production server. That is, an agent which gets commands from the gitlab server and executes them. This runner needs full root access (or, equivalently, docker daemon access), thus giving the gitlab server (and anyone who has/gains control over it) full root access to the production system(s).
    This approach also needs meticulous management of the different runners, since they are now being used not just for build purposes but also have a second, distinct, duty for deployment.
  2. Use your normal gitlab runners that are running somewhere else, but explicitly give them root access to the target servers, e.g. with a remote SSH login.
    Again, this gives everybody in control of the gitlab server full production access, as well as anybody in control of one of the affected runners. Usually this is made less obvious by “only” giving docker daemon access, but that’s still equivalent to full root access.

There’s variants on this theme, like using Ansible for some abstraction, but it always boils down to somehow making it so that the gitlab server is capable of executing arbitrary commands as root on the production system.

Our Approach

For container management we’re going to use docker compose, the new one, not docker-compose. A compose.yaml file (with extensions, see below) is going to fully describe the deployment, and compose will take care of container management for updates.

Ideally we want to divide the task into two parts:

  1. Initial setup
  2. Continuous delivery

For the initial setup there’s not a pressing reason for full automation. We’re not setting up new environments all the time. There’s still some best practices and room for automation, see below, but in general it’s a one-time process executed with high privileges.

The continuous updates on the other hand should be fast, automated, and, above all, restricted. An update to a deployed docker application does exactly one thing: pull new image(s) then restart container(s).

Restricted SSH keys for update deployment

Wouldn’t it be great if we had an agent on the production server that could do that, and only that? Turns out, we have! Using additional configuration on ~/.ssh/authorized_keys we can configure a public key authenticated login that will only execute a (set of) predefined command(s), and nothing else1. And since sshd is already running and exposed to the internet anyway, we don’t get any new attack surface.

The options we need are:

  • restrict to disable, roughly, all other functionality
  • command="cd ...; docker compose pull && docker compose up -d" which will make any login with that key execute only this command (you’ll need to fill in the path to cd into).

Using docker compose

In order for this to seamlessly work, there’s some best practices to follow when creating the environment:

  • All container configuration is handled by the docker compose framework 
    • Specifically: docker compose up just works.
      No weird docker compose -f compose.foo.yaml -f compose.bar.yaml -e WTF_AM_I_DOING=dunno up incantations.
  • The docker compose configuration should itself be version controlled
  • The containers come up by themselves in a usable configuration, and can handle container updates gracefully 
    • For example in Django, the django-admin migrate call must be part of the container startup
    • In general it’s not allowed to need to manually execute commands in the containers or the compose environment for updates. You’re allowed to require one necessary initialization command on first setup, under extenuating circumstances only.
  • There’s also good container design (topic of a different blog post) with regards to separation of code and data

Good docker compose setup

There’s two ways to handle the main compose configuration of a project: As part of the git repository of one of the components, or as a separate git repository by itself.

  • The first approach applies if it’s a very simple project, maybe just one component. If it contains only the code you wrote, and possibly some ancillary containers like the database, then you’ll put the compose.yaml into the root directory of the main git repository. This also applies if your project consists of multiple components maintained by you, but it’s obvious which one is the main one (usually the most complex one).
    Like if you have a backend container (e.g. Python wsgi), a frontend container (statically compiled HTML/JS, hosted by an nginx), a db container (standard PostgreSQL), maybe a cache, and some helper daemon (another Python project). Three of these are maintained by you, but the main one is the backend, so that’s where the compose.yaml lives.
  • For complex projects it makes sense to create a dedicated git repository that only hosts the compose file and associated files. This specifically applies if the compose file needs to be accompanied by additional configuration files to be mounted into the containers. These usually do not belong in your application’s git repository.

The idea here is that the main compose.yaml file (using includes is allowed) handles all the basic configuration and setup of the project, independent of the environment. Doing a docker compose up -d should bring up the project in some default state configured for a default environment (e.g. staging). Additional environment specific configuration should be placed in a compose.override.yaml file, which is not checked into git and which contains all the modifications necessary for a specific environment. Usually this will only set environment variables such as URL paths and API keys.

Additional points of note:

  • All containers should be configured read_only: true, possibly assisted by tmpfs: ["/run","/var/run/someapp"] or similar. If that’s not possible, go yell at the container image creator2.
  • All configuration that is mounted from the outside should be mounted read-only
  • Data paths are handled by volumes
  • The directory name is the compose project name. That’s how you get the ability to deploy more than one instance of a project on the same host. The directory name should be short and to the point (e.g. frobnicator or maybe frobnicator-staging).
  • Ports in the main compose.yaml file are a problem, since port numbers are a global resource. A useful pattern is to not specify a port binding in the compose.yaml file and instead rely on compose.override.yaml for each deployment to specify a unique port for this deployment. That’s one of the few cases where it’s acceptable to absolutely require a compose.override.yaml for correct operation, and it must be noted in the README.

Putting It All Together

This example shows how to set up deployment for project transmogrify/frobnicator, hosted on gitlab at git.example.com, with the registry accessible as registry.example.com, to host deploy-host, using non-root (but still docker daemon capable) user deploy-user.

Preliminaries

On deploy-host, we’ll create a SSH public/private key to be used as deploy key for the git repository containing the main compose.yaml and configure docker pull access. This probably only needs to be done once for each target host.

ssh-keygen -t ed25519

Just hit enter for default filename (~/.ssh/id_ed25519) and no passphrase. Take the resulting public key (in ~/.ssh/id_ed25519.pub) and configure it in gitlab as a read-only deploy key for the project containing the compose.yaml (under https://git.example.com/transmogrify/frobnicator/-/settings/repository).

We’ll also need a deploy token for docker registry access. This should be scoped to access all necessary projects. In general this means you’ll want to keep all related projects in a group and create a group access token under https://git.example.com/groups/transmogrify/-/settings/access_tokens. Create the access token with a name of deploy-user@deploy-host, role Reporter and Scope read_registry.

Caveat 1: docker can only manage one set of login credentials per registry host. Either use non-privileged/user-space docker daemons separated by project (e.g. with different users on the deploy host, each one only managing one project), which is a topic of a different blog post. Or use a “Personal” Access Token for a global technical user which has access to all the necessary projects instead.
(There’s a third option: Create multiple .docker/config.json files and set the DOCKER_CONFIG environment variable accordingly. This violates the “docker compose up should just work” requirement.)

Caveat 2: docker really doesn’t want to store login credentials at all. There’s a couple of layers of stupidity here. Just do the following (note: this will overwrite all previously saved docker logins on this host, but you shouldn’t have any):

mkdir -p ~/.docker
echo '{"auths": {"registry.example.com": {"auth": ""}}}' > ~/.docker/config.json

Then you can do a normal login with the deploy token and it’ll work:

docker login registry.example.com

First deployment / Setup

Clone the repository and configure any overrides necessary. Then start the application.

git clone ssh://git@git.example.com/transmogrify/frobnicator.git
cd frobnicator
vi compose.override.yaml  # Or whatever is necessary
docker compose up -d

Your project should now be running. Finish any remaining steps (set up reverse proxy etc.) and debug whatever mistakes you made.

Set up for autodeploy

  1. On deploy-host (or a developer laptop) generate another SSH key. We’re not going to keep it for very long, so do it like this:
ssh-keygen -t ed25519 -f pull-key  # Hit enter a couple of times for no passphrase
  1. Also retrieve the SSH host keys from deploy host (possibly through another way):
ssh-keyscan deploy-host
  1. On deploy-host, add the following line to ~deploy-user/.ssh/authorized_keys (where ssh-ed25519 AAA... is the contents of pull-key.pub from step 1):
restrict,command="cd /home/deploy-user/frobnicator; docker compose pull && docker compose up -d" ssh-ed25519 AAA.... 
  1. In gitlab, on group level, configure variables (https://git.example.com/groups/transmogrify/-/settings/ci_cd):
NameValueSettings
SSH_AUTH-----BEGIN OPENSSH PRIVATE ...
(contents of pull-key from step 1)
File, Protected
SSH_KNOWN_HOSTSresults of step 2
SSH_DEPLOY_TARGETssh://deploy-user@deploy-host

You may use the environments feature of gitlab here, which will generally mean a different set of values per environment (and then choosing the environment in the job in the next step). Afterwards, delete the temporary files pull-key and pull-key.pub from step 1.

  1. In your project’s or projects’ .gitlab-ci.yaml file (this is in the code projects, not necessarily the project containing compose.yaml), add this (the publish docker job is outside of the scope of this post):
stages:
  - build
  - deploy

# ...

deploy docker:
  image: docker:git
  stage: deploy
  cache: []
  needs:
    - publish docker
  before_script: |
    mkdir -p ~/.ssh
    echo "${SSH_KNOWN_HOSTS}" > ~/.ssh/known_hosts
    chmod -R go= ~/.ssh "${SSH_AUTH}"
  script: ssh -i "${SSH_AUTH}" -o StrictHostKeyChecking=no "${SSH_DEPLOY_TARGET}"

In order to handle multiple environments, you can also add

deploy docker:
  # ...
  rules:
    - if: $CI_COMMIT_BRANCH == "dev"
      variables:
        ENVIRONMENT: staging
    - if: "$CI_COMMIT_BRANCH =~ /^master|main$/"
      variables:
        ENVIRONMENT: production  
  environment: "${ENVIRONMENT}"

Voila. Every time after a docker image has been built, a gitlab runner will now trigger a docker compose pull/up, with minimal security impact since that’s the only thing it can do.

Addenda

The preliminaries and initial setup can be automated with Ansible.

You can put the gitlab-ci configuration into a common template file that can be referenced from all projects. For example, we have a common tools/ci repository, so the only thing necessary to get auto deployment is to put

stages:
  - publish
  - deploy

include:
  - project: tools/ci
    file: docker-ssh.yml

into a project and do the deploy-host setup and variable definitions (well, and add the other include files that handle the actual docker image building).

  1. man authorized_keys, section “AUTHORIZED_KEYS FILE FORMAT↩︎
  2. Their image is bad and they should feel bad. ↩︎

Should I Use JWTs For Authentication Tokens?

No.


Not satisfied? Fine, fine. I’ll write a longer answer.

Let’s talk about what we’re talking about. JWT stands for JSON Web Tokens, a reasonably well defined standard for authenticated tokens. Specifically they have a header with format information, a payload, and a signature or message authentication code. The core idea is that whoever has the corresponding verification key can verify that the payload is authentic and has not been altered. What they do with that information is up to them.

The JWT spec (RFC 7519) makes suggestions by providing a few well-known registered claim names: issuer, audience, subject, expiration time, etc. A common usage pattern is that, after verifying the authenticity against whatever trust relationship they have with the issuer, the recipient checks whether they are the intended audience (if any is specified) and the expiration time has not yet passed, and then take the subject as an authenticated identity of the bearer of the token.

It’s perfectly designed for bearer token authentication! Or is it? Let me be clear: JWT as authentication tokens are constructed for Google/Facebook scale environments, and absolutely no one who is not Google/Facebook needs to put up with the ensuing tradeoffs. If you process less than 10k requests per second, you’re not Google nor are you Facebook.

The core benefit, proponents will tell you, is that the recipient of a JWT doesn’t need to connect to the user database to verify the token authenticity and render its service. In a large installation, like Google’s, that means that the JWT issuer, the authentication service, can be a dedicated service that is managed and scaled like other services, and is the only service that needs to access the centralized user database. All other services can act on the information stored in the JWT alone, and don’t need to go through the user database, which would represent a choke point.

What about logout/session invalidation? Well, in order for this model to work, the authentication token should have a fairly short lifetime. Maybe 5 minutes, max. The client is also issued a second token, the so-called refresh token, with which it can request a new authentication token from the authentication service. This gives the authentication service a chance to consult the user database to see whether the user or a specific session has been blocked in the meantime.

Here’s the twist that is rarely, if ever, spelled out: In this setup the refresh token, not the authentication token, is the real session token. The refresh token represents the session with the authentication service (which can be revoked), while the authentication tokens are just derived credentials to be used for a few requests at most. The beauty, from Google’s point of view, is that this delegates keeping the session alive to the client, i.e. not Google’s servers. Oh and by the way, the refresh token can be, and usually is, opaque, since it’s only ever consumed by the same service that creates it. That reduces a lot of complexity, by just using an opaque identifier stored in a database.

Now, let’s assume you are not Google. Check which of these apply to you:

  • You wanted to implement log-out, so now you’re keeping an allowlist of valid JWTs, or a denylist of revoked JWTs. To check this you hit the database on each request.
  • You need to be able to block users entirely, so you check a “user active” flag in the database. You hit the database on each request.
  • You need additional relationships between the user object and other objects in the database. You hit the database on each request.
  • Your service does anything at all with data in the database. You hit the database on each request.

Congratulations, if you confirmed any of the items above, you don’t need JWTs. You’re hitting the database anyway, and I’m pretty sure that you only have one database which stores both your user profiles and your application data. By just using a “normal” opaque session token and storing it in the database, the same way Google does with the refresh token, and dropping all JWT authentication token nonsense, you stand to reap these great benefits:

  • No weird workarounds (allow/denylist) for shortcomings of JWT as authentication token
  • Greatly reduced complexity. No need to manage a secure JWT signing/authentication key
  • You get to pass on some interesting bugs.

Just use the normal session mechanism that comes with your web framework and that you were using before someone told you that Google uses JWT. It has stood the test of time and is probably fine.

If you need something to do to make you feel like you’re running a big deployment, you can probably configure your session mechanism to use redisvalkey to store the session data. You’re still going to use the authenticated user id to query the database, but for unauthenticated requests it may be faster/use less resources. It might not be. You’ll have to tune and measure that.

On the Security of AES in HomeMatic

HomeMatic is a line of home automation devices that are popular in Germany and use a proprietary radio protocol (BidCoS, Bidirectional Communication Standard) on a frequency of 868MHz. Some devices allow optional use of “AES signing” for message authentication. When enabled, the execution of a command is delayed until a challenge-response process between the initiator and receiver of the command is completed. All AES capable HomeMatic devices ship with a default key, which can optionally be set to a custom value. The signing requirement is disabled by default for most devices, except for the KeyMatic/WinMatic line which includes devices for door lock and window automation which always require AES for all commands.

Before 2014 the common wisdom1 was to leave the AES key at the default value: Setting a custom key and forgetting it renders the device useless and requires it to be sent back to the manufacturer to reset it – for a fee.

Sathya Laufer and Christian Mallas demonstrated that this is trivially dangerous at the 30th Chaos Communication Congress: Within the HomeMatic universe there are LAN gateways that accept commands over Ethernet/IP and forward them through BidCoS to the target device. In this setup, all the BidCoS AES operations are executed by the LAN gateway. So if the target device is using the default key, an attacker can simply use a LAN gateway (which also knows the default AES key) to send arbitrary commands to that device, without bothering with any of the cryptographic details.

Later, the default AES key became known, and a reverse engineering of the authentication protocol is available (see next section), so an attacker can also use custom hardware to send commands to all devices still configured with the default key.

Michael Gernoth did a superb job of reverse engineering the authentication protocol2, but his description is mainly based on the flow to authenticate a known message. I’ll try to re-frame that here in the way that the authentication token is generated, and also generically for arbitrary message sizes. Where applicable I’ll use the same abbreviations that Michael uses (∥ is concatenation, ⊕ is XOR):

Packets
m Original message to be authenticated. Note: m = D0 ∥ D1, if the length is considered not to be part of the packet.
c Challenge message
r Response message
a ACK message
Data items
Name Description Length/bytes Packet
D0 Metadata (counter, flags, type, sender, receiver) 10 m
D1 Parameters varies m
C Challenge 6 c
P AES response 16 r
A ACK authentication 4 a
T Timestamp or counter 6

Under these definitions the calculation of the authentication messages happens as follows:

  • K’ := K ⊕ (C ∥ 00…)
  • Pd’ := AES(K’, T ∥ D0)
  • P := AES(K’, Pd’ ⊕ (D100…) )

Or, in easier terms, and likely representing the original idea, for arbitrary packet lengths:

AESCBC(IV= 00…, Key= K ⊕ (C ∥ 00…), Payload= T ∥ m)

(with the last block of the CBC calculation being output as P and the first 4 bytes of the first block as A)

While this looks like a standard (ab)use of AES-CBC as an authentication code, best as I can tell, the verification really has to happen in the very strange backwards way that Michael describes, for the simple reason that T is not transmitted and thus not available to the verifier to replicate the same calculation as the initiator.

How secure is the HomeMatic system if a custom AES key is used?

A customary simple measure for security is the number of bits an attacker must guess correctly to violate whatever security properties the system claims to have. This is related to the number of operations to execute in the attack3. Example: For an authentication mechanism with a security of 128 bits, the attacker needs to either correctly guess 128 bits (either key or authentication code) to break the system, or perform 2127 operations (random authentication attempts) to score a successful attack with a probability of ~50%.4

Under this definition a single block of AES with fully random key has a security of 128 bits. We would hope to find the same security in the HomeMatic use of AES.

CBC-MAC is generally not a recommended way to construct a message authentication code and very easy to implement wrong, for examples and a longer discussion of this aspect see this article on CBC-MAC by Matthew Green. That being said, XORing the challenge into the key prevents most of the more obvious attacks. They are enabled again, when a challenge is re-used though.

Challenge re-use

The challenge is a 6‑byte value generated by the verifier that must be random. Random number generation (RNG) with computers is hard, and RNGs in many (embedded) devices have had spectacular failures with security implications in the past: 1 2 3 4.

Now, I can’t make any assertions as to the quality of HomeMatic’s RNG, so let’s assume that it is stellar and always outputs full 48 bits of randomness. The birthday paradox then tells us that after around 224 ≈ 16.7 million tries there’s a better than even (50%) chance that a number repeats. A repeated challenge directly gives the attacker the possibility to replay a previous command.

For this attack to work with probability 50% the attacker must capture on the order of 16.7 million identical messages, and then try to get the same command executed 16.7 million times, representing a security factor of around 26 bits. Probably nothing to worry about in a radio protocol that will do at most ~5 authentications per second (the second phase alone will take 6 weeks), but a far cry from 128 bits.

The specific usage of CBC-MAC is also susceptible to an attack where the attacker can craft a valid authentication token for a message that consists of a message sniffed from the radio channel appended with attacker controlled data, see Matthew’s article linked above. However, I couldn’t think of a way to make this stick: The attacker still needs to be able to either coax the system into generating an authentication token for a different attacker chosen message, or be very limited in the message manipulations possible.

Blind guessing

Remember that T is never transmitted but apparently inferred from the protocol? That means, from an attacker’s point of view, that these bits are free. Instead of guessing 128 bits of key or authentication code, the attacker only need to guess 128–6*8=80 bits. Even if the verifier checks T to be monotonously increasing that only adds one additional bit of complexity. It works like this:

  1. Attacker sends arbitrary message m = D0 ∥ D1
  2. Verifier sends challenge C
  3. Attacker sends random answer P
  4. Verifier calculates
    • Pd’ := AES-1(K’,P) ⊕ (D100…), and
    • Pd’d := AES-1(K’, Pd’).

    Verifier then checks whether the last ten bytes of Pd’d match D0 (and maybe if the first 6 bytes are higher than the last T received).

  5. If in the previous step a match is found, the verifier executes the command and outputs A.

Note that in the protocol D1 is never checked, but used to calculate something that is checked against D0. Now: An attacker that feeds random data into P will cause random data to appear in Pd’d. There’s a chance of 1 in 280 that this random data matches D0 (and possibly: another 1 in 2 that the first 6 bytes are numerically larger than the last T).

Again: 80 is much lower than 128, so cryptographically speaking the mechanism is broken. Practically though, sending 280 requests (giving a success probability of 63% to the attacker) will take 7.6 Pa5, so that’s probably nothing to worry about.

Entropy

Internally the AES key is a binary value of 128 bits, but that’s not how it’s presented to the user in the front-end. Setting the HomeMatic security key requests an arbitrary text string from the user, which is then hashed with MD5 and the resulting hash is used as the AES key.

A careless user might not worry too much about this key, and the on-screen prompt only reminds them to use at least 5 characters. Even under the best of circumstances, one typeable character has only about 6 bits of entropy. The minimum security recommended by the user interface therefore is equivalent to 5*6 = 30 bits. Also: An attacker can execute an offline dictionary attack on the key after intercepting one or a few radio messages. Execution rates for these kinds of attacks typically lie in the millions or billions of operations per second even on regular PC hardware (CPU and GPU), so any 5 character security key will be cracked in seconds.

Luckily this isn’t a flaw by design: The user needs to make sure to chose a fully random key with at least 128 bits of entropy, for example in the form of 32 random hexadecimal characters.

Summary

The problems are, in order from most to least exploitable:

  1. Low entropy in the security key. Security break in seconds. Easily averted by choosing a strong key.
  2. Challenge re-use. Break may be possible within a few decades to years.
  3. Blind guessing. Break possible within a few petayears.

From a theoretical point of view, the security of the BidCoS protocol with a custom AES key is much worse than it should be. From a practical point of view it’s entirely acceptable, if the user chooses a long fully random key, and the attacker isn’t present when the key is set6.

Note: These considerations only apply to the theoretical protocol and not any particular implementation. It’s possibly, even likely, that there are exploitable bugs in some device firmware and/or that the RNG is not as good as expected. Bugs in this area generally reduce the security to the “break within minutes to seconds” category.


  1. See for example this archived article from October 2012, courtesy of the internet archive. The article was updated to say the polar opposite in January 2014. 

  2. XORing the challenge into the key is somewhat unusual, I’m not sure I would’ve found that 

  3. Theoretical computer science generally doesn’t care about constant factors. 

  4. These two notions are not identical, but close enough that, for the purposes of approximately judging system security, I’ll treat them as interchangeable for this article. 

  5. 7.6 petayears, 7 600 000 000 000 000 years 

  6. Obviously the key is transmitted over the air, encrypted with a key that the attacker already knows by induction.