pip install cognis-graphqlmap
graphqlmap scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ graphqlmap-emit --version
graphqlmap 0.1.0

$ graphqlmap-emit --help
usage: graphqlmap [-h] [--version] {analyze} ...

Analyze a GraphQL introspection result for risky fields, excessive depth, and
authorization gaps (defensive / authorized use only).

positional arguments:
  {analyze}
    analyze   Analyze an introspection JSON file.

options:
  -h, --help  show this help message and exit
  --version   show program's version number and exit

Blocks above are real graphqlmap output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
  "findings": [
    {
      "id": "123456",
      "title": "Suspicious Network Traffic",
      "description": "Potential malicious activity detected on network port 8080",
      "created_at": "2023-02-15T14:30:00Z",
      "updated_at": "2023-02-15T14:30:00Z",
      "labels": ["network", "malware"],
      "observables": [
        {
          "type": "ip-dst",
          "value": "192.168.1.100"
        },
        {
          "type": "port",
          "value": 8080
        }
      ]
    }
  ]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install graphqlmap
   

2. Capture a target's introspection result (the __schema query response) to a JSON file, then analyze its attack surface:

   graphqlmap analyze introspection.json
   

3. Tune the depth threshold and the severity gate, or write a self-contained HTML report:

   graphqlmap analyze introspection.json --depth-threshold 8 --fail-on MEDIUM
   graphqlmap analyze introspection.json --format html -o report.html
   

4. Read the output: the table lists each finding (severity, id, title, location, fix). For tooling, use --format json and parse findings[] and severity_counts. The process exits non-zero when a finding meets or exceeds --fail-on.
5. Gate a CI job on the schema (fails the step on MEDIUM+):

   graphqlmap analyze introspection.json --format json --fail-on MEDIUM > gql.json
   

Defensive / authorized use only.

Contents

• Why graphqlmap? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why graphqlmap?

Analyze GraphQL introspection for risky fields, depth, and authz gaps — without standing up heavyweight infrastructure.

graphqlmap is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Load Introspection
• ✅ Analyze Introspection
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-graphqlmap
graphqlmap --version
graphqlmap scan .                       # scan current project
graphqlmap scan . --format json         # machine-readable
graphqlmap scan . --fail-on high        # CI gate (non-zero exit)

Example

$ graphqlmap scan .
  [HIGH    ] GRA-001  example finding             (./src/app.py)
  [MEDIUM  ] GRA-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[graphqlmap<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

graphqlmap is interoperable with every popular way of using AI:

• MCP server — graphqlmap mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe graphqlmap scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis graphqlmap	typical tools
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (graphqlmap mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/graphqlmap.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/graphqlmap.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/graphqlmap.git" # uv
pip install cognis-graphqlmap                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/graphqlmap:latest --help        # Docker
brew install cognis-digital/tap/graphqlmap                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/graphqlmap/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/graphqlmap	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If graphqlmap saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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