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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ payloadlab-emit --version
payloadlab 0.1.0

$ payloadlab-emit --help
usage: payloadlab [-h] [--version] {scan} ...

Static malicious payload analyzer (PE/ELF/LNK/macro/OneNote).

positional arguments:
  {scan}
    scan      statically analyze one or more files

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

Blocks above are real payloadlab 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 port 443.",
        "created_at": "2023-02-16T14:30:00Z",
        "updated_at": "2023-02-16T14:30:01Z",
        "labels": ["Network", "Malware"],
        "confidence": 0.8,
        "severity": "Medium"
    }
]
}

Usage — step by step

payloadlab is a static malicious-payload analyzer for PE/ELF/LNK/macro/OneNote files (static analysis only — it never executes samples). Exit codes escalate by verdict: 0 clean, 1 low-risk, 2 suspicious, 3 malicious.

1. Install:

   pip install -e .
   payloadlab --version
   

2. Scan one or more files:

   payloadlab scan sample.bin invoice.lnk
   

3. Read the output as JSON (per-file verdict and indicators):

   payloadlab scan sample.bin --format json | jq '.[].verdict'
   

4. Gate on severity — make the process fail at or above a chosen verdict:

   payloadlab scan ./quarantine/* --fail-on suspicious
   

5. Automate in CI / a sandbox intake — branch on the exit code:

   payloadlab scan "$f" --fail-on malicious && echo clean || echo "flagged: $?"
   

Contents

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

Why payloadlab?

Static malicious payload analyzer — PE/ELF/LNK/macro/OneNote — without standing up heavyweight infrastructure.

payloadlab 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

• ✅ Shannon Entropy
• ✅ Detect Format
• ✅ Score Verdict
• ✅ Analyze Bytes
• ✅ Analyze File
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

payloadlab is interoperable with every popular way of using AI:

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

Related Cognis tools

• c2detect — C2 server fingerprinter — Cobalt Strike, Sliver, Mythic, Havoc, Brute Ratel
• redpath — Active Directory attack path mapper — minimum-cost paths + remediation priority
• 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

Contributing

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

⭐ If payloadlab 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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