Darkmirror

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ darkmirror-emit --version
darkmirror 0.1.0

$ darkmirror-emit --help
usage: darkmirror [-h] [--version] [--format {table,json}]
                  {watch,diff,stats} ...

Surface-web mirror of a public Tor leak-site index for brand monitoring.

positional arguments:
  {watch,diff,stats}
    watch               match a snapshot against a brand watchlist
    diff                show posts newly added between two snapshots
    stats               summarize a snapshot

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

Blocks above are real darkmirror 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 interface 192.168.1.100.",
        "created_at": "2023-02-15T14:30:00Z",
        "updated_at": "2023-02-15T14:30:00Z",
        "objects": [
            {
                "id": "obj123456",
                "type": "indicator",
                "name": "Malicious IP Address",
                "description": "Potential malicious activity detected on network interface 192.168.1.100.",
                "created_at": "2023-02-15T14:30:00Z",
                "updated_at": "2023-02-15T14:30:00Z"
            }
        ]
    }
]
}

Usage — step by step

1. Install the CLI (Python 3.9+):

   pip install darkmirror     # or: pip install .   from a checkout
   

2. Watch a snapshot against your brand watchlist — the watch subcommand fuzzy-matches a surface-web leak-index snapshot (JSON) against terms:

   darkmirror watch snapshot.json --term acme-corp --term acme.com
   darkmirror watch snapshot.json --watchlist brands.txt --threshold 0.82
   

3. Diff two snapshots to surface newly-added posts, or summarize one:

   darkmirror diff old.json new.json
   darkmirror stats snapshot.json
   

4. Read the result — the global --format json flag emits structured matches with scores for alerting:

   darkmirror --format json watch snapshot.json -w brands.txt | jq '.matches[] | {term, score}'
   

5. Run periodically and alert on new hits — diff against the prior snapshot in a scheduled job:

   darkmirror --format json diff prev.json latest.json | jq -e '.added_count == 0'
   

Contents

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

Why darkmirror?

Surface-web mirror of public Tor leak-site index for brand monitoring — without standing up heavyweight infrastructure.

darkmirror 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 Posts
• ✅ Normalize Posts
• ✅ Match Watchlist
• ✅ Diff Snapshots
• ✅ Summarize
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[sources] --> P[darkmirror<br/>curate + validate]
  P --> OUT[query / analysis]

Use it from any AI stack

darkmirror is interoperable with every popular way of using AI:

• MCP server — darkmirror mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe darkmirror 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 darkmirror	joshhighet
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 joshhighet/ransomwatch, 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 (darkmirror 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/darkmirror.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/darkmirror.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/darkmirror.git" # uv
pip install cognis-darkmirror                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/darkmirror:latest --help        # Docker
brew install cognis-digital/tap/darkmirror                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/darkmirror/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/darkmirror	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• personagraph — Identity resolution dossier — username/email/phone cross-platform
• maritimeint — AIS vessel tracking & sanctions-evasion anomaly detection
• geolens — Image geolocation toolkit — EXIF, sun-shadow, OCR, reverse-search
• corpmap — Corporate structure & beneficial-ownership mapper
• cryptotrace — Free-tier blockchain investigator — ETH/BTC clustering + sanctions xref

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 darkmirror 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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