pandoraspec 0.3.7

DORA Compliance Auditor for OpenAPI Specs

PanDoraSpec

The Open DORA Compliance Engine for OpenAPI Specs.

PanDoraSpec is a CLI tool that performs deep technical due diligence on APIs to verify compliance with DORA (Digital Operational Resilience Act) requirements. It compares OpenAPI/Swagger specifications against real-world implementation to detect schema drift, resilience gaps, and security issues.

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💡 Why PanDoraSpec?

1. Compliance as Code

DORA audits are often manual, annual spreadsheets. PanDoraSpec provides Continuous Governance, proving that every commit has been verified for regulatory requirements (Drift, Resilience, Security).

2. The "Virtual CISO" Translation

Developers see HTTP 500. Executives see "Article 25 Violation". Module E translates technical failures into Regulatory Risk Scores, bridging the gap between DevOps and the Boardroom.

3. Zero-Config Guardrails

It requires no configuration to catch critical issues. It acts as a safety net that catches schema drift and leaked secrets before they hit production.

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📦 Installation

pip install pandoraspec

System Requirements

The PDF report generation requires weasyprint, which depends on Pango.

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🚀 Usage

Run the audit directly from your terminal.

Basic Scan

pandoraspec https://petstore.swagger.io/v2/swagger.json

With Options

pandoraspec https://api.example.com/spec.json --vendor "Stripe" --key "sk_live_..."

Local File

pandoraspec ./openapi.yaml

Override Base URL

If your OpenAPI spec uses variables (e.g. https://{env}.api.com) or you want to audit a specific target:

pandoraspec https://api.example.com/spec.json --base-url https://staging.api.example.com

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⚙️ Configuration

🧙 Configuration Wizard

Get started quickly by generating a configuration file interactively:

pandoraspec init

This will guide you through creating a pandoraspec.yaml file with your target URL, vendor name, and seed data templates.

Configuration File (pandoraspec.yaml)

You can store your settings in a YAML file:

target: "https://petstore.swagger.io/v2/swagger.json"
vendor: "MyVendor"
api_key: "my-secret-key"
# Avoid False Positives in DLP by allowing support emails
dlp_allowed_domains:
  - "mycompany.com"
seed_data:
  user_id: 123

Precedence Rules:

1. CLI Arguments (Highest Priority)
2. Configuration File
3. Defaults (Lowest Priority)

Example: pandoraspec --vendor "CLI Override" --config pandoraspec.yaml will use the target from YAML but the vendor "CLI Override".

✅ Validate Configuration

Ensure your configuration file is valid before running an audit:

pandoraspec validate --config pandoraspec.yaml

🔐 Dynamic Authentication (Hooks)

For complex flows (OAuth2, MFA, etc.) that require logic beyond a static API Key, you can use a Pre-Audit Hook. This runs a custom Python script to acquire a token before the audit starts.

1. Create a script (auth_script.py) that returns your token as a string:

import os
import requests

def get_token():
    # Example: Fetch token from an OAuth2 endpoint
    response = requests.post("https://auth.example.com/token", data={
        "client_id": os.getenv("CLIENT_ID"),
        "client_secret": os.getenv("CLIENT_SECRET"),
        "grant_type": "client_credentials"
    })
    return response.json()["access_token"]

2. Configure pandoraspec.yaml:

target: "https://api.example.com/openapi.json"
auth_hook:
  path: "auth_script.py"
  function_name: "get_token"

PanDoraSpec will execute get_token(), take the returned string, and use it as the Authorization: Bearer <token> for all audit requests.

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🧪 Testing Modes

🏎️ Zero-Config Testing (Compliance)

For standard DORA compliance, you simply need to verify that your API implementation matches its specification. No configuration is required.

pandoraspec https://petstore.swagger.io/v2/swagger.json

This runs a fuzzing audit where random data is generated based on your schema types.

🧠 Advanced Testing (Seed Data)

To test specific business workflows (e.g., successfully retrieving a user profile), you can provide "Seed Data". This tells PanDoraSpec to use known, valid values instead of random fuzzing data.

pandoraspec https://petstore.swagger.io/v2/swagger.json --config seed_parameters.yaml

[!NOTE] Any parameters NOT explicitly defined in your seed data will continue to be fuzzed with random values. This ensures that you still get the benefit of stress testing on non-critical fields while controlling the critical business logic.

Configuration Hierarchy

The engine resolves values in this order: Endpoints > Verbs > General.

seed_data:
  # 1. General: Applies to EVERYTHING (path params, query params, headers)
  general:
    username: "test_user"

  # 2. Verbs: Applies only to specific HTTP methods
  verbs:
    POST:
      username: "admin_user"

  # 3. Endpoints: Applies only to specific routes
  endpoints:
    /users/me:
      GET:
        limit: 10

🔗 Dynamic Seed Data (Recursive Chaining)

You can even test dependency chains where one endpoint requires data from another.

endpoints:
  # Level 1: Get the current user ID
  /user/me:
    GET:
      authorization: "Bearer static-token"

  # Level 2: Use that ID to get their orders
  /users/{userId}/orders:
    GET:
      userId:
        from_endpoint: "GET /user/me"
        extract: "data.id"

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🛡️ What It Checks

Module A: The Integrity Test (Drift)

Checks if your API implementation matches your documentation.

• Why? DORA requires you to monitor if the service effectively supports your critical functions.

Module B: The Resilience Test

Stress tests the API to ensure it handles invalid inputs gracefully (4xx vs 5xx).

• Why? DORA Article 25 calls for "Digital operational resilience testing".

Module C: Security Hygiene & DLP

Checked for:

• Security headers (HSTS, CSP, etc.)
• Auth enforcement on sensitive endpoints.
• Data Leakage Prevention (DLP): Scans responses for PII (Emails, SSNs, Credit Cards) and Secrets (AWS Keys, Private Keys).

Module E: AI Auditor (Virtual CISO)

Uses OpenAI (GPT-4) to perform a semantic risk assessment of technical findings.

• Requires: OPENAI_API_KEY environment variable.
• Output: Generates a Risk Score (0-10) and an Executive Summary.
• Configuration:
  • export OPENAI_API_KEY=sk-...
  • Override model: --model gpt-3.5-turbo

Module D: The Report

Generates a PDF report: "DORA ICT Third-Party Technical Risk Assessment".

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🏭 CI/CD

PanDoraSpec is designed for automated pipelines. It returns Exit Code 1 if any issues are found, blocking deployments if needed.

🐳 Docker

Run without installing Python:

docker run --rm -v $(pwd):/data ghcr.io/0d15e0/pandoraspec:latest https://petstore.swagger.io/v2/swagger.json --output /data/verification_report.pdf

🐙 GitHub Actions

Add this step to your.github/workflows/pipeline.yml:

- name: DORA Compliance Audit
  uses: 0D15E0/PanDoraSpec@v0.2
  with:
    target: 'https://api.example.com/spec.json'
    vendor: 'MyCompany'
    format: 'junit'
    output: 'dora-results.xml'

📊 JUnit Reporting

Use --format junit to generate standard XML test results that CI systems (Jenkins, GitLab, Azure DevOps) can parse to display test pass/fail trends.

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🛠️ Development

Local Setup

To run the CLI locally without reinstalling after every change:

1. Clone & CD:

git clone ...
cd pandoraspec

2. Create & Activate Virtual Environment:

python3 -m venv venv
source venv/bin/activate

3. Editable Install:

pip install -e .

📦 Publishing (Release Flow)

This repository uses a Unified Release Pipeline.

1. Update Version: Open pyproject.toml and bump the version (e.g., version = "0.2.8"). Commit and push.
2. Draft Release:
   • Go to the Releases tab in GitHub.
   • Click Draft a new release.
   • Create a tag MATCHING the version (e.g., v0.2.8).
   • Click Publish release.

The workflow will verify version consistency and automatically publish to Docker (GHCR) and PyPI.

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📄 License

MIT