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terraform-guard

AI-agent-native Terraform plan safety gate.

terraform-guard checks the JSON output of a Terraform/OpenTofu plan before apply and returns a deterministic verdict:

• allow: no dangerous change detected
• warn: risky change needs human review
• block: likely data loss, exposure, outage, or privilege blast radius

It is designed for the new failure mode: coding agents can now edit infrastructure and may try to run terraform apply. This tool gives agents, CI systems, and humans a machine-readable pre-apply stop sign.

Quick Start

pip install-e".[dev]"
terraform plan -out=tf.plan
terraform show -json tf.plan > plan.json
terraform-guard check plan.json --pro--format text

Write PR/CI artifacts:

terraform-guard check plan.json --pro\
 --markdown-file terraform-guard.md \
 --sarif-file terraform-guard.sarif \
 --json-file terraform-guard.json

CI-friendly exit codes:

• 0: allow
• 1: parse/input error
• 2: warn when --fail-on warn
• 3: block

MCP

Run the local MCP server:

$terraform-guard-mcp

MCP tool:

• terraform_guard_check_plan(plan_json, ruleset)
• terraform_guard_list_rules(ruleset)

Local Pro mode is gated by TFGUARD_LICENSE_KEY. The current offline demo accepts keys beginning with TFG-PRO-; production licensing must replace this with signed offline licenses or marketplace verification.

GitHub Action

This repository includes a root action.yml for GitHub Action distribution:

-run: terraform plan -input=false -out=tf.plan
-run: terraform show -json tf.plan > tfplan.json
-uses: your-org/terraform-guard@v0
with:
plan-path: tfplan.json
pro:"true"
fail-on: block
upload-sarif:"true"

Examples:

• docs/examples/github-action-plan-gate.yml
• docs/examples/github-action-apply-gate.yml
• docs/examples/gitlab-ci.yml
• docs/examples/atlantis.yaml

The action writes JSON, Markdown, and SARIF reports under terraform-guard-output/.

Apify

This repo includes an Apify Actor scaffold:

.actor/actor.json
.actor/input_schema.json
.actor/output_schema.json
.actor/openapi.json
.actor/pay_per_event.json
Dockerfile

The Actor supports two modes:

• normal Actor run: paste planJson, get a dataset/KV result
• Standby mode: exposes a Streamable HTTP MCP endpoint at /mcp

Pay-per-event hooks are wired for:

• terraform-plan-check
• terraform-pro-ruleset

Configure the same events in Apify Console when publishing.

Rules

Free rules:

Pro rules:

Recommended Product Lane

Do not position this as another static Terraform scanner. Checkov and Trivy already own broad IaC scanning.

The wedge is narrower and sharper:

pre-apply approval gate for AI-generated infrastructure changes.

Best distribution targets:

• local CLI and MCP for Cursor, Claude, Codex, VS Code, and other agents
• GitHub/GitLab CI gates with PR comments
• Atlantis, Spacelift, env0, and Terraform Cloud run-task integrations
• Apify as an agent marketplace and pay-per-event experiment

The moat should be low-false-positive plan risk scoring, repo/workspace baselines, approval evidence, and integrations in the actual apply path.

More detail:

• docs/CI_APPLY_PATH_INTEGRATIONS.md
• docs/MONETIZATION_AND_REGISTRATION.md
• docs/PRODUCT_STRATEGY.md

Development

pip install-e".[dev]"
pytest
ruff check .

Privacy

The core scanner runs locally and does not call cloud APIs. Terraform plans can contain sensitive infrastructure metadata, so local CLI/MCP and self-hosted CI should be the primary enterprise deployment path. Hosted Apify runs are best for demo, marketplace discovery, and teams comfortable uploading plan JSON.