Ssrfind

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ ssrfind-emit --version
ssrfind 0.1.0

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

Find SSRF-prone sinks and unvalidated URL fetches in source code (defensive
static analysis).

positional arguments:
  {scan}
    scan      Scan a file or directory tree for SSRF hotspots.

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

Blocks above are real ssrfind output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"Findings": [
    {
        "id": "1234567890",
        "title": "Example Finding 1",
        "description": "This is an example finding.",
        "severity": "high",
        "labels": ["malware", "ransomware"],
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "2345678901",
        "title": "Example Finding 2",
        "description": "This is another example finding.",
        "severity": "medium",
        "labels": ["phishing", "spear-phishing"],
        "created_at": "2023-02-16T10:45:00Z"
    }
]
}

Usage — step by step

1. Install the CLI (console script ssrfind):

   pip install cognis-ssrfind
   

2. Scan source for SSRF sinks — scan walks a file or directory tree for SSRF-prone sinks and unvalidated URL fetches:

   ssrfind scan ./src
   

3. Tune severity / format — only surface higher-confidence hits, or emit JSON:

   ssrfind scan ./src --min-severity high
   ssrfind scan ./src --format json > ssrf.json
   

4. Read the output — each finding carries severity, file:line, the rule_id, the sink/argument, and explanatory notes; the JSON adds a summary count by severity. Exit 1 when findings exist, 0 when clean, 2 on a bad path:

   ssrfind scan ./src --format json | jq '.summary, (.findings[] | {rule_id, file, line})'
   

5. Automate in CI — fail the job when SSRF hotspots appear:

   - run: pip install cognis-ssrfind
   - run: ssrfind scan ./src --min-severity medium  # nonzero exit = findings
   

Contents

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

Why ssrfind?

SSRF hotspots

ssrfind 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

• ✅ Scan Source
• ✅ Scan Path
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

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

Use it from any AI stack

ssrfind is interoperable with every popular way of using AI:

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