REDPATH

Active Directory attack path mapper — minimum-cost paths + remediation priority

Red Team / Offensive — adversary tooling for authorized engagements.

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ redpath-emit --version
redpath 1.0.0

$ redpath-emit --help
usage: redpath [-h] [--version] [--format {table,json}] {paths,remediate} ...

Active Directory attack path mapper: minimum-cost compromise paths and
remediation priority.

positional arguments:
  {paths,remediate}
    paths               map minimum-cost attack paths to targets
    remediate           rank edges by remediation priority

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format

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

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

{
"Findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Network Traffic",
        "description": "Potential malicious activity detected on network port 80.",
        "mitre_attack_id": ["T1204"],
        "ttp_stix_id": ["attack-pattern--1-2-3"]
    },
    {
        "id": "2345678901",
        "title": "Unusual File Access",
        "description": "File access patterns indicate potential data exfiltration.",
        "mitre_attack_id": ["T1008"],
        "ttp_stix_id": ["attack-pattern--4-5-6"]
    }
]
}

Usage — step by step

1. Install (Python 3.9+):

   pip install redpath            # or: pipx install redpath

2. Enumerate attack paths from an attack-graph JSON file:

   redpath paths graph.json

3. Get prioritized remediation for the same graph:

   redpath remediate graph.json

4. Get machine-readable output. The global --format flag goes before the subcommand:

   redpath --format json paths graph.json > paths.json

5. Read the result. paths lists the routes an attacker can take through the graph; remediate ranks the cut-points that break the most paths. Parse the JSON to feed a ticketing or dashboard system.

6. Wire it into CI. Regenerate the path analysis whenever the modeled graph changes:

   redpath --format json remediate graph.json > remediation.json

Contents

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

Why redpath?

Active Directory attack path mapper — minimum-cost paths + remediation priority — without standing up heavyweight infrastructure.

redpath 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.

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Features

• ✅ Load Graph
• ✅ Shortest Path
• ✅ Map Attack Paths
• ✅ Remediation Priority
• ✅ Load Graph File
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

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

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Example

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

  2 findings · risk score 5 · 38ms

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Architecture

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

Loading

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Use it from any AI stack

redpath is interoperable with every popular way of using AI:

• MCP server — redpath mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe redpath 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

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How it compares

	Cognis redpath	BloodHoundAD
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

Built in the spirit of BloodHoundAD/BloodHound, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

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

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/redpath.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/redpath.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/redpath.git" # uv
pip install cognis-redpath                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/redpath:latest --help        # Docker
brew install cognis-digital/tap/redpath                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/redpath/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/redpath	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• c2detect — C2 server fingerprinter — Cobalt Strike, Sliver, Mythic, Havoc, Brute Ratel
• payloadlab — Static malicious payload analyzer — PE/ELF/LNK/macro/OneNote
• pwnreview — Pentest report generator — YAML findings to CREST-grade PDF
• crackq — Self-hosted password cracking queue — multi-user hashcat with audit log

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

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Contributing

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

⭐ If redpath 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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