Ssltriage

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ ssltriage-emit --version
ssltriage 0.1.0

$ ssltriage-emit --help
usage: ssltriage [-h] [--version] {grade} ...

Grade TLS config (protocols/ciphers/expiry) from openssl/sslyze output.

positional arguments:
  {grade}
    grade     Grade TLS output from a file or stdin

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

Blocks above are real ssltriage 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": "Network traffic from unknown IP address",
        "severity": "medium",
        "created_by": "John Doe",
        "created_at": "2023-02-20T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Unusual File Access",
        "description": "File access from unknown user",
        "severity": "high",
        "created_by": "Jane Smith",
        "created_at": "2023-02-21T10:45:00Z"
    }
]
}

Usage — step by step

1. Install the CLI (console script ssltriage):

   pip install cognis-ssltriage
   

2. Grade TLS output — pipe openssl/sslyze output into grade (or pass a file; - = stdin) to get a TLS report card:

   openssl s_client -connect example.com:443 -showcerts </dev/null | ssltriage grade -
   

3. Annotate the target / emit JSON — label the host and produce machine-readable output:

   ssltriage grade scan.txt --target example.com --format json > tls.json
   

4. Read the output — the report has grade (A–F), score/100, worst_severity, negotiated protocols, cert_days_remaining, and a findings list (code/severity/message). Exit 1 once a finding meets the --fail-on threshold (default medium), else 0:

   ssltriage grade scan.txt --format json | jq '.grade, .cert_days_remaining'
   

5. Automate in CI — fail on actionable TLS findings:

   - run: pip install cognis-ssltriage
   - run: ssltriage grade scan.txt --fail-on high
   

Contents

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

Why ssltriage?

TLS report card

ssltriage 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 Input
• ✅ Grade Report
• ✅ Triage
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

ssltriage is interoperable with every popular way of using AI:

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

Related Cognis tools

• portfan — Summarize and diff nmap XML into prioritized, attackable findings
• subhunt — Aggregate & dedupe subdomain enumeration from multiple sources
• 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

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 ssltriage 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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