devZero-Security/redStackPRO

A web canvas where you build infrastructure as a topology, then export a complete, runnable working directory of Terraform and Ansible. You run it from your own machine. redStackPRO never holds your cloud credentials.

redStackPRO puts attack infrastructure and target ranges on the same canvas. The two canvas modes are Offense (attack infrastructure) and Defense (defensive AD ranges); the export names its handoff OFFENSE-BRIEFING.md or DEFENSE-BRIEFING.md to match.

Split horizon C2, attack infrastructure: two front doors that do not share a fate, Apache fronting Sliver and Nginx fronting Mythic, each redirector on its own peered network, with the teamservers, collector, and operators behind a jumpbox.

Multiple operators, one stack. An attack-infrastructure jumpbox can name a list of operators (handle plus role), and each gets a Guacamole portal login on the shared lab password. Set the jumpbox's access_mode to wireguard or openvpn (default is a public portal) and every operator also gets a personal VPN credential generated on the jumpbox at apply: the keys never leave the box or enter the export, only the client config file does. The portal logins today share the one lab password, so per-operator isolation comes from each operator's own VPN credential, not the portal login; individual portal passwords are on the roadmap. On a VPN access mode the portal closes to the internet and moves behind the tunnel, while SSH stays open so the admin can keep deploying and managing the box. Add or remove a teammate on a running jumpbox with sudo rsp-operator add <handle>. See the wiki Deploying a Range.

Harbor, a target range: a small corporate forest, a root domain and a child over a parent-child trust, with the ordinary path from a phished workstation to the forest.

GOAD, the full lab: three domains across two forests, five machines and their trusts, the reference range the written solution follows.

Pre-release, with more to come before 1.0. The pipeline itself works end to end: a topology compiles to Terraform and Ansible, and the export deploys.

The whole canvas in one container, the API and the web app on one port:

docker compose up                    # builds from this repo, http://127.0.0.1:8000

Or pull the published image instead of building it:

docker run -p 8000:8000 -v redstackpro-data:/data \
  ghcr.io/devzero-security/redstackpro:latest

The canvas listens on 8000 inside the container. To serve it on a different host port, change the left half of the mapping (-p 8787:8000), or set REDSTACKPRO_PORT for compose (REDSTACKPRO_PORT=8787 docker compose up).

Compose also carries an optional Postgres backend for a shared deployment:

REDSTACKPRO_DATABASE_URL=postgresql+psycopg://redstackpro:redstackpro@db:5432/redstackpro \
  docker compose --profile postgres up

The image is the composition layer only. It does not carry Terraform or Ansible and never holds your cloud credentials: you run the export it produces from your own machine, exactly as in the from-source flow below.

Python 3.11 or newer, and Node 24 for the canvas.

git clone <this repo> && cd redStackPRO
python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"

The canvas is two processes, the API and the web app:

redstackpro serve                            # http://127.0.0.1:8000
cd frontend && npm install && npm run dev

redstackpro serve --port 8787 moves the API to a different port. Point the canvas dev server at it with REDSTACKPRO_API=http://127.0.0.1:8787.

Open it, use Load blueprint (the button, or the mode palette) to open a shipped starting point, choose your cloud in the toolbar provider selector (GCP or AWS), open the Export tab (it compiles as you go), then Download. You get a zip of the working directory described below.

Or skip the canvas entirely and compile a shipped blueprint from the command line, same compiler, same output:

redstackpro compile frontend/public/goad/goad-light.json -o export

The command line defaults to GCP. Pass --provider aws for AWS.

That writes about 200 files: Terraform for the cloud, Ansible for everything that happens on the boxes, a deploy.sh, and a DEFENSE-BRIEFING.md telling you the credentials and what is planted where. goad-light is a two-domain Active Directory range: sevenkingdoms and its child north, a parent-child trust in one forest, two domain controllers and a member server, plus a jumpbox.

aws configure
aws sts get-caller-identity

gcloud auth login
gcloud auth application-default login
gcloud config set project <project-id>
gcloud services enable compute.googleapis.com --project <project-id>

To deploy it, fill in export/deploy.tfvars (at the export root) and run:

cd export && bash deploy.sh

deploy.sh copies deploy.tfvars into terraform/terraform.tfvars at apply time and aborts if deploy.tfvars is missing, so edit the root file, not the one under terraform/.

On Windows, run .\deploy.ps1 instead: bash deploy.sh at a PowerShell prompt launches WSL, a different filesystem with different credentials. See the wiki Deploying a Range for detail.

deploy.sh applies the Terraform and then provisions from the range's own jumpbox, because the managed hosts sit on a private subnet nothing else can reach. Your cloud credentials stay on your machine throughout.

Every run writes a timestamped, secret-scrubbed log to logs/deploy-<timestamp>.log in the export. If a deploy fails or a range comes up wrong, attach the newest one to a GitHub issue; deploy.sh prints its path and a link when something goes wrong.

Check it worked. The export ships verify.py next to deploy.sh. Run it against the deployed range to confirm the path end to end:

python verify.py doors .     # redirector front doors, from outside
python verify.py stack .     # the range over SSH from the jumpbox

Each exits 0 when every check passes. On an offense range, the generated OFFENSE-BRIEFING.md holds the C2 payload recipe (callback domain, URI prefix, and gating header per redirector), and sudo rsp-check on a redirector checks the same path from the box itself.

Manage and tear down. The export also ships lifecycle scripts, manage.sh (and manage.ps1 on Windows), for the running range. They take status, start, stop, and teardown: stop pauses billing without destroying the range, and teardown destroys it.

./manage.sh status
./manage.sh start
./manage.sh stop
./manage.sh teardown

AWS quotas are per region, and a compile does not check them. Scope them before a large or concurrent deploy, so an apply does not fail partway and leave infrastructure running and billing.

• Elastic IPs. The default is 5 per region. A defense/AD range uses about 2 (jumpbox plus NAT gateway), an offense range about 3 (jumpbox, redirector, NAT gateway), so the default holds only one or two concurrent ranges. Request an increase per region in advance. An EIP shortfall surfaces at provisioning, after the instances are up, so a failed apply leaves them running to be destroyed.
• vCPUs. The running On-Demand standard vCPU limit caps a region. Hosts are mostly t3.large and t3.medium (2 vCPU each), and a full AD range (GOAD plus a SIEM) can exceed the default. Check and raise it before large deploys.
• Kali subscription. A Kali operator needs a one-time Marketplace subscription on the account, per region. redStackPRO generates code and touches no account, so you subscribe once per region you deploy Kali into.

All of this is per region. Running several ranges at once? Give each a distinct prefix so its host names stay clear; the few account-global names (the key pair and the auto-stop role) auto-suffix per deployment, so ranges never collide in one account. The full quota table, the increase commands, and the pre-deploy checklist are in the wiki: Providers and Deploying a Range.

Everything the canvas does is available headless, so an agent can operate redStackPRO without a person clicking through it. A topology is plain JSON against a published schema (src/redstackpro/schema/topology/, also served at /api/v1/registry/schema), the registry endpoints report the vocabulary of node kinds, providers, and roles, and the validate and compile steps the canvas calls are the same REST endpoints. The API is documented at /docs and /openapi.json, which a model can read as tools.

redstackpro serve                                  # http://127.0.0.1:8000
curl 127.0.0.1:8000/api/v1/registry/schema         # the topology schema
curl -X POST 127.0.0.1:8000/api/v1/validate \
  -H 'content-type: application/json' \
  -d '{"document": {...topology...}, "provider": "gcp"}'
curl -X POST 127.0.0.1:8000/api/v1/compile \
  -H 'content-type: application/json' \
  -d '{"document": {...topology...}, "provider": "gcp"}'

Validation returns structured findings, each with a stable code, the target, prose and a remedy, and the compiler refuses a document with errors and hands back those same findings. The loop is read the schema, compose a topology, validate, fix what the findings name, compile. A model iterates on the outcome rather than scraping prose, and the validate call stores nothing along the way. The command line is the same pipeline for a shell agent, redstackpro validate and redstackpro compile.

The running instance trusts the local caller, which suits a single-user or self-hosted deployment. Authenticated, multi-tenant agentic access over the API is part of the enterprise edition. redStackPRO holds no cloud credentials on any path: the export is the handoff, and you run it yourself.

redStack proved the pattern but the topology is fixed in code. redStackPRO makes it composable, adds multi-provider support, and puts attack infrastructure and target ranges on the same canvas.

• docs/architecture.md
• docs/schema.md
• docs/validation.md
• docs/solutions/
• src/redstackpro/schema/topology/

src/                     the Python package and everything it ships
  redstackpro/           the composition layer: validator, compiler, API
    schema/              the document schema, registry data, worked examples
    tools/               command line tools (compile, validate, dev tooling)
    assets/              Ansible roles and static files baked into the export
frontend/                the web canvas (Vite + React)
tests/                   the suite: compiler, validator, API, export
docs/                    architecture, schema, validation, solutions, images
pyproject.toml           package metadata, deps, and the console entry points
alembic.ini              migration config for the Postgres backend
Dockerfile               the one-container build (API plus built canvas)
docker-compose.yml       local run, with an optional Postgres profile
.dockerignore            build context trim
README.md                this file
LICENSE                  MIT, with the GOAD templates under GPLv3
.github/                 CI workflows

MIT. See LICENSE.

The GOAD range templates under frontend/public/goad/ are the exception: they are derived from GOAD (GPLv3) and are distributed under GPLv3, with the license and attribution in that directory. They are data, not code: the canvas loads them as native ranges that compile through redStackPRO's own terraform and ansible. The rest of the project is MIT.

A devZero Security Project.