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This document explains the theory behind Data Theorem’s Private Network Proxy offering, and as well as instructions for setting it up.

Use-Case and Architecture

Data theorem’s API Secure product is a SAAS offering.

In order to be able analyze APIs and services that are on a private network, Data Theorem needs a proxy/connector that gives Data Theorem’s analyzer engine access to that private network. DT’s Private Network Proxy is an appliance, provided as a Docker image, that can set up a network tunnel and a proxy that allows Data Theorem to access services on the private network.

The appliance uses SSH to connect back to Data Theorem and establish a port forwarding “tunnel”, and it provides a proxy that allows the analyzer engine can access from within its VPC.

[diagram of architecture]

Setting up a private-network-proxy Appliance

These instructions are for the initial “v1” implementation. Data Theorem expects to refine and improve the setup flow with future releases.

Summary

  • Plan out how you want to deploy the appliance

  • Create an SSH keypair, and provide the public key to DT support.

  • DT will prepare the private-network-proxy service for the appliance and will provide some additional configuration parameters

  • Set up a host to run the appliance as a Docker container

  • Configure the container with the SSH keypair and the other necessary parameters

Planning out the Deployment

Data Theorem’s private-network-proxy is provided as a Docker image that can be deployed to a host with access to a private environment that contains APIs that are not publicly addressable. There are several requirements for how this image should be deployed:

  • The container must be able to perform DNS lookups of the APIs that will be scanned. For simple deployments, if the host system for a container can resolve hostnames in the private network, so can the container.

  • The container must be configured with an SSH private key and a port assigned to the appliance by Data Theorem. The sections below discuss coordinating this configuration with Data Theorem.

  • A particular appliance’s container should only exist once – it should be scaled or replicated across a cluster (eg, Docker Swarm or Kubernetes). The container is an appliance, and that instance represents where network traffic from Data Theorem will originate within the private network.

  • If you have multiple isolated private networks where there you have APIs to scan, then each network will need its own appliance/container configured with Data Theorem.

The appliance’s Docker container image is available at gcr.io/datatheorem-public-images/private-network-proxy-client-v1

Configuration

The appliance needs an SSH keypair to be able to connect to Data Theorem, identify itself, and associate it with the APIs hosted in its private network. This keypair should be kept safe and secure, but it will need to be configured and accessible to the appliance’s container.

The appliance also requires a port number that Data Theorem must provide. This is an internal configuration parameter that is bound to the appliance, but the appliance must request it due to how SSH’s port forwarding works.

1. Create an SSH keypair

The keypair should be created without a password to encrypt it, but Data Theorem recommends taking steps to protect the private key – depending on how the container is hosted, you may be able to use a secrets feature of Docker, Kubernetes, etc.

The keys must be stored in OpenSSH’s format, and not in some other public key format (eg, PKCS8 or PEM).

Creating a ED25519 keypair:

ssh-keygen -t ed25519 -C "name for my appliance" -f my_keyfile  # This will create my_keyfile and my_keyfile.pub

Alternately, create an RSA keypair:

ssh-keygen -t rsa -b 3072  -C "name for my appliance" -f my_keyfile  # This will create my_keyfile and my_keyfile.pub

When prompted to set a password, leave it blank and press enter. You can set a password to encrypt the private key if you want the extra protection, but you will need to remove the password later when the private key is provided to the appliance’s Docker container.

Store the private key file somewhere safe until you are ready to configure the Docker container.

2. Send the public key to Data Theorem

Email the public key file to support@datatheorem.com along with a brief explanation of your use-case for the appliance. Eg, explain whether this appliance will access a dev or staging environment, or some other use-case.

Data Theorem will use the public key to set up the service in our own infrastructure and assign a port to it. We will then reply with the value needed for the PROXY_PORT ENV value for the container.

3. Configure the container

In order to configure the container you must provide it with at least the SSH private key and the PROXY_PORT. The private key can be supplied through a Docker ENV variable, or it can be provided as a file on the filesystem (eg, if you want to mount the file or a volume, or if you use a secrets manager that can mount files).

The image is available at: gcr.io/datatheorem-public-images/private-network-proxy-client-v1

Required ENV variables

The following ENV variable is always required:

  • PROXY_PORT – The SSH client uses this value to set up a reverse port forward on the server. The server restricts which server port(s) a client may ask to use, but this parameter is necessary because the client must ask to set up the port to forward its traffic to the proxy.

Just one of the following ENV variables should be specified:

  • SSH_PRIVATE_KEY_DATA – The raw data of the SSH private key for the appliance. It uniquely identifies the appliance and the proxy it provides. The key data must not be encrypted with a password.

    • If you are unable to specify newline characters when setting this key’s value, the container has special support for replacing a \n character sequence with a newline character – if you use this ENV variable you can replace any newlines in the private key with a \n. Container orchestration that specifies configuration through YAML files usually can specify newlines, but specifying values through command line arguments or .env files may not be able to easily include newlines in the ENV values.

  • SSH_PRIVATE_KEY_FILE – A path to the private key file within the container. Offered as an alternative to specifying the contents of the private key file directly, this allows you to interoperate with various key/secrets managers, or if you want to mount the key file from a volume or from the host.

    • The file must be readable by the appliance user within the container because the container does not run as root. The container’s logs will print the user’s UID when it first starts up.

Optional ENV variables

The following ENV variables should not be explicitly set unless you want to change the behavior of the container.

  • RETRY_AFTER_DISCONNECT – Whether the appliance should automatically reconnect if something happens to the connection to the server. Defaults to yes. If it is set to no the container will exit if SSH ever disconnects.

  • VERBOSITY – How verbose the container’s output is. Defaults to 1. Set this to 0 for almost no output, or 1, 2, or 3 for increasingly verbose levels of output. Only set it to a higher level if you need to do some sort of low-level debugging of the SSH connection.

  • SERVER_HOST – Override the host that the container tries to connect to.

  • SERVER_PORT – Override the server port that the container tries to connect to.

  • SERVER_PUBKEY – Override the public key of the remote server.

Examples

Run the appliance directly with Docker using SSH_PRIVATE_KEY_DATA

PRIVATE_KEY_FILE="/path/to/private_key"
PROXY_PORT=10123

# Replace newline characters with a \n character sequence:
PRIVATE_KEY_DATA=`cat ${PRIVATE_KEY_FILE}| while read line ; do echo -n "${line}\\n" ; done`

docker run -it \
    -e "PROXY_PORT=${PROXY_PORT}" \
    -e "SSH_PRIVATE_KEY_DATA=${PRIVATE_KEY_DATA}" \
    gcr.io/datatheorem-public-images/private-network-proxy-client-v1:latest

Run the appliance directly with Docker using SSH_PRIVATE_KEY_FILE

PRIVATE_KEY_FILE="/path/to/private_key"
PROXY_PORT=10123

# bind-mount the private key into the container. The private key file must be
# readable by the low-rights user within the container -- the appliance within the
# container does not run as root. Eg, you may have to chmod the private key file,
# or grant access to the container-user's group/gid from on the host system.
docker run -it \
    -e "PROXY_PORT=${PROXY_PORT}" \
    -e "SSH_PRIVATE_KEY_FILE=/private_key" \
    --mount "type=bind,src=${PRIVATE_KEY_FILE},dst=/private_key,readonly=true" \
    gcr.io/datatheorem-public-images/private-network-proxy-client-v1:latest

Run the appliance on GCP’s Compute Engine, within a Container Optimized OS

GCP provides a convenient way to spin up a VM that runs a single Docker container without having to worry about orchestrating

Create a somefilename.env file that contains the ENV configuration for the appliance. Note that the newlines in the private key have been replaced with \n to include it on a single line, due to a limitation of the file format:

PROXY_PORT=10123
SSH_PRIVATE_KEY_DATA=-----BEGIN OPENSSH PRIVATE_KEY-----\n...\n-----END OPENSSH PRIVATE KEY-----

Then create the VM using GCP’s gcloud command line tool:

gcloud --project=${PROJECT} compute instances create-with-container \
    my-vm-name \
    --container-image gcr.io/datatheorem-public-images/private-network-proxy-client-v1:latest \
    --container-env-file=client1_vm.env \
    ...  # Any additional flags for creating the VM, such as network tags, zone, etc.

Security Architecture

Client Security

The appliance container’s security primarily depends on how/where it is deployed and configured. However, Data Theorem has take steps to minimize the attack surface of the client and follow container best practices:

  • The service that runs in the container does not run as root. The Docker image runs commands as a normal, non-root user, minimizing what code running in the container can modify, and minimizing concerns about root processes running within containers.

  • The Docker image is based on Alpine Linux, which is known for having a significantly smaller footprint compared to other popular distributions. It also relies on Linux hardening features like PIE, and it uses MUSL as its libc instead of GNU’s libc.

  • The SSH client is configured to only trust a specific server key, instead of the default of prompting or auto-trusting new keys for a new host. (via a UserKnownHostsFile and the StrictHostKeyChecking option)

Server Security

The server component is also run in a container in a VM on GCP.

  • The VM runs Google’s Container Optimized OS (COS), which is a hardened, minimal OS optimized for deploying individual docker containers on GCP. GCP also builds its GKE (Kubernetes) offering and other container-based offerings on COS.

  • The VM is firewalled to only publicly expose a non-standard SSHD port

  • The container runs SSHD and nothing else

  • The container runs SSHD as a non-root user

  • SSHD is configured to lock down what SSH features and services it offers

    • In addition to being run as a non-root user, it is configured to disallow root logins and to disable password-based authentication entirely

    • It disables all SSH sub-services except for remote/reverse port forwarding

    • It only allows authorized keys to authenticate

    • It restricts each authorized key to disable running commands, and to disable all services except reverse port forwarding

    • Each authorized key is granted a single port that it can open that SSHD will listen on to receive traffic meant for the proxy running in the client

  • The SSHD server’s private key is kept out of source code. Instead, it is protected using GCP’s Secrets Manager, and it is only accessed in order to deploy it to the VM and provide it to the container.

  • The proxy ports are only accessible to a VPC that is restricted to the security scanner components of Data Theorem’s analyzer engine that need traffic to originate from a static IP address, or that may need to go through the private-network-proxy

  • The VM is hosted on a GCP project separate from any other Data Theorem services, isolating it from other services, and making it easier to manage who has internal access

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