Subhunt

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ subhunt-emit --version
subhunt 0.1.0

$ subhunt-emit --help
usage: subhunt [-h] [--version] {merge} ...

Aggregate & dedupe subdomain enumeration output from multiple sources into one
clean set (defensive / authorized testing).

positional arguments:
  {merge}
    merge     Merge subdomain source files/dirs into one deduped set.

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

Blocks above are real subhunt 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": "Possible malicious activity detected on port 443",
        "created_at": "2023-02-15T14:30:00Z",
        "updated_at": "2023-02-15T14:30:01Z",
        "labels": ["network", "suspicious"],
        "observables": [
            {
                "type": "ip-dst",
                "value": "192.0.2.1"
            },
            {
                "type": "port",
                "value": 443
            }
        ]
    }
]
}

Usage — step by step

1. Install the CLI (console script subhunt):

   pip install cognis-subhunt
   

2. Merge enumeration output — merge takes one or more source files/dirs (one host per line, # comments) and produces one deduped set:

   subhunt merge amass.txt subfinder.txt assetfinder.txt
   

3. Scope and format — restrict to a registrable domain and emit JSON:

   subhunt merge ./results/ --scope example.com --format json > subs.json
   

4. Read the output — each host lists its source count and which sources reported it; the JSON stats block reports unique, duplicates, invalid, and out_of_scope. Exit code is 1 when unique hosts were found, 0 when nothing usable parsed:

   subhunt merge *.txt --scope example.com --format json | jq '.stats'
   

5. Automate in a recon pipeline — feed the deduped set straight into the next tool:

   subhunt merge sources/*.txt --scope example.com | awk '{print $1}' | httpx
   

Contents

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

Why subhunt?

one clean subdomain set

subhunt 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

• ✅ Normalize Host
• ✅ Is Valid Hostname
• ✅ In Scope
• ✅ Parse Source
• ✅ Aggregate
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[target / export] --> P[subhunt<br/>collect + correlate]
  P --> OUT[ranked findings]

Use it from any AI stack

subhunt is interoperable with every popular way of using AI:

• MCP server — subhunt mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe subhunt 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 subhunt	amass
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 amass/subfinder, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (subhunt 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/subhunt.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/subhunt.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/subhunt.git" # uv
pip install cognis-subhunt                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/subhunt:latest --help        # Docker
brew install cognis-digital/tap/subhunt                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/subhunt/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/subhunt	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• portfan — Summarize and diff nmap XML into prioritized, attackable findings
• dirsight — Analyze web content-discovery output (ffuf/gobuster) into ranked endpoints
• jwtinspect — Decode JWTs and lint for alg=none, weak secrets, and missing claims
• corsaudit — Detect permissive/misconfigured CORS from headers or a config
• headerscan — Grade HTTP security headers (CSP/HSTS/XFO) A-F from a response dump
• ssltriage — Grade TLS config (protocols/ciphers/expiry) from openssl/sslyze output

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 subhunt 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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