Mftparse

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ mftparse-emit --version
mftparse 0.1.0

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

Analyze an NTFS $MFT CSV export for timestomping and suspicious file activity
(defensive forensics).

positional arguments:
  {analyze}
    analyze   Analyze an $MFT CSV and report findings.

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

Blocks above are real mftparse 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 Activity",
        "description": "An unknown device connected to our network.",
        "created_by": "John Doe",
        "created_at": "2023-02-15T14:30:00Z"
    }
]
}

Usage — step by step

mftparse analyzes an NTFS $MFT CSV export for timestomping and suspicious file activity (defensive forensics). Console script: mftparse.

1. Install from a clone:

   pip install -e .
   

2. Analyze an $MFT CSV (use - to read from stdin):

   mftparse analyze mft_export.csv
   

3. Write a shareable HTML report:

   mftparse analyze mft_export.csv --format html -o mft_report.html
   

4. Read the output — --format json is pipeline-friendly; exit code is 1 when findings exist, 0 when clean:

   mftparse analyze mft_export.csv --format json | jq '.findings'
   

5. Automate in CI / triage pipelines — gate on detected timestomping:

   cat collected_mft.csv | mftparse analyze - --format json > findings.json
   

Contents

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

Why mftparse?

Analyze an NTFS $MFT CSV for timestomping and suspicious file activity — without standing up heavyweight infrastructure.

mftparse 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

• ✅ Parse Mft Csv
• ✅ Analyze
• ✅ Render Table
• ✅ Render Json
• ✅ Render Html
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[disk / memory artifact] --> P[mftparse<br/>parse]
  P --> OUT[timeline + IOCs]

Use it from any AI stack

mftparse is interoperable with every popular way of using AI:

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