Corsaudit

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

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ corsaudit-emit --version
corsaudit 0.1.0

$ corsaudit-emit --help
usage: corsaudit [-h] [--version] [--format {table,json}] {headers,config} ...

Detect permissive/misconfigured CORS from captured response headers or a
config. Defensive analysis only -- performs no network requests.

positional arguments:
  {headers,config}
    headers             Analyze a captured 'Name: value' response-header
                        block.
    config              Analyze a declarative CORS config from a JSON file.

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 corsaudit output — reproduce them from a clone.

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

{
"findings": [
    {
        "id": "123456",
        "title": "Suspicious Connection",
        "description": "A potential security issue was detected.",
        "severity": "medium",
        "created": "2023-02-15T14:30:00Z"
    },
    {
        "id": "789012",
        "title": "Malware Detection",
        "description": "A malicious file was found on the system.",
        "severity": "high",
        "created": "2023-02-16T10:45:00Z"
    }
]
}

Usage — step by step

1. Install:

   pip install -e .
   

2. Analyze captured response headers with the headers subcommand (reads a file or - for stdin). Pass --origin to enable reflection checks against the probe Origin you sent:

   curl -sD - -H 'Origin: https://evil.example' https://api.example.com -o /dev/null \
     | corsaudit headers - --origin https://evil.example
   

3. Or audit a declarative CORS config (JSON file or stdin) with the config subcommand:

   corsaudit config cors.json
   

4. Read the result. Each finding shows a RULE, SEVERITY, title, the offending origin, detail, and a fix: remediation. Use --format json for piping; the process exits 1 when any finding is reported, 0 when clean, 2 on usage error. corsaudit performs no network requests — it only analyzes input you give it.

   corsaudit headers headers.txt --format json
   

5. Use it in CI — fail the pipeline on any CORS misconfiguration:

   corsaudit config cors.json --format json || { echo "CORS misconfig"; exit 1; }
   

Contents

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

Why corsaudit?

catch wildcard+credentials

corsaudit 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 Header Block
• ✅ Analyze Headers
• ✅ Analyze Config
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

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

Example

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

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[target / manifest] --> P[corsaudit<br/>checks + rules]
  P --> OUT[findings (JSON / SARIF)]

Use it from any AI stack

corsaudit is interoperable with every popular way of using AI:

• MCP server — corsaudit mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe corsaudit 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 corsaudit	CORScanner
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 CORScanner, 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 (corsaudit 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/corsaudit.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/corsaudit.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/corsaudit.git" # uv
pip install cognis-corsaudit                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/corsaudit:latest --help        # Docker
brew install cognis-digital/tap/corsaudit                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/corsaudit/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/corsaudit	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
• headerscan — Grade HTTP security headers (CSP/HSTS/XFO) A-F from a response dump
• ssltriage — Grade TLS config (protocols/ciphers/expiry) from openssl/sslyze output

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 corsaudit 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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