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API Security Framework

$39

API authentication, rate limiting, input validation, CORS policies, and automated security testing for REST/GraphQL.

📁 29 files
MarkdownJavaScriptConfigPythonYAMLDjangoFlaskNginx

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📁 File Structure 29 files

api-security-framework/ ├── LICENSE ├── README.md ├── config/ │ ├── cors-policy.example.yaml │ └── nginx-security.conf ├── docs/ │ ├── api-security-testing-checklist.md │ ├── authentication-authorization.md │ ├── cors-and-headers.md │ ├── input-validation.md │ ├── rate-limiting.md │ └── secret-handling.md ├── examples/ │ ├── express_integration.js │ └── wsgi_demo.py ├── free-sample.zip ├── guide/ │ ├── 01-understanding-api-threat-models.md │ ├── 02-authentication-authorization-deep-dive.md │ ├── 03-rate-limiting-and-input-validation.md │ ├── 04-security-headers-cors-and-secret-handling.md │ └── 05-deployment-hardening-and-testing-checklist.md ├── index.html └── src/ ├── node/ │ ├── cors.js │ ├── jwt_verify.js │ ├── rate_limiter.js │ └── security_headers.js └── python/ ├── input_validation.py ├── jwt_auth.py ├── rate_limiter.py ├── secrets_loader.py └── security_headers.py

📖 Documentation Preview README excerpt

API Security Framework

Drop-in, dependency-free building blocks and the guidance to use them, for

securing an HTTP / REST API. Every control that usually gets bolted on too late --

authentication, authorization, rate limiting, input validation, CORS, security

headers, and secret handling -- is here as small, readable, unit-tested code in

both Python (standard library only) and Node.js (built-ins only), paired with

a focused guide and a copy-into-your-tracker testing checklist.

Nothing here needs pip install or npm install to run (the one exception is the

optional Express example, which is clearly marked). You can read every line,

drop a module into a service, and test it.

Table of Contents

1. [Who this is for](#who-this-is-for)

2. [What's included](#whats-included)

3. [Quick start](#quick-start)

4. [How the pieces fit together](#how-the-pieces-fit-together)

5. [File-by-file guide](#file-by-file-guide)

6. [Design principles](#design-principles)

7. [Using it in your stack](#using-it-in-your-stack)

8. [FAQ](#faq)

9. [License](#license)

10. [Support](#support)

Who this is for

  • Backend / API engineers who want correct, copy-pasteable implementations of

the security controls every API needs.

  • Security engineers reviewing or hardening an API and looking for a reference

for "what good looks like".

  • Tech leads standardizing auth, rate limiting, and headers across services.
  • Learners who want to understand JWT verification, CORS, and rate limiting

by reading short, honest code rather than a 10,000-line library.

Assumes working knowledge of HTTP and either Python or JavaScript. No prior

security specialization required -- the docs explain the why.

What's included

AreaDocsPython (stdlib)Node (built-ins)
Authentication / authorization (JWT, OAuth2)authentication-authorization.mdjwt_auth.pyjwt_verify.js
Rate limiting & abuse preventionrate-limiting.mdrate_limiter.pyrate_limiter.js
Input validation & output handlinginput-validation.mdinput_validation.py—
CORS & security headerscors-and-headers.mdsecurity_headers.pycors.js, security_headers.js
Secret handlingsecret-handling.mdsecrets_loader.py—
Testingapi-security-testing-checklist.mdexamples/wsgi_demo.pyexamples/express_integration.js

Plus reverse-proxy hardening (config/nginx-security.conf), a CORS policy file

(config/cors-policy.example.yaml), and a placeholder .env.example.

... continues with setup instructions, usage examples, and more.

📄 Code Sample .py preview

src/python/input_validation.py #!/usr/bin/env python3 """Allow-list input validation with dependency-free, composable validators. Most injection and data-integrity bugs come from trusting input. These validators implement the safe model: **positive (allow-list) validation**, server-side, that fails closed and returns only the fields you explicitly declared -- which also defeats mass-assignment / over-posting. from input_validation import ( ValidationError, validate_schema, is_email, is_uuid, in_range, string_length, one_of, ) SCHEMA = { "email": {"required": True, "validator": is_email}, "age": {"required": False, "validator": in_range(0, 120)}, "username": {"required": True, "validator": string_length(3, 32)}, "role": {"required": True, "validator": one_of("user", "editor", "admin")}, } clean = validate_schema(payload, SCHEMA) # raises ValidationError on bad input Run directly for a self-test: python3 input_validation.py """ from __future__ import annotations import re import uuid from datetime import datetime # A deliberately conservative email shape. Validation confirms structure; it does # not guarantee deliverability (only sending a verification mail does). _EMAIL_RE = re.compile(r"^[A-Za-z0-9._%+\-]{1,64}@[A-Za-z0-9.\-]{1,255}\.[A-Za-z]{2,}$") class ValidationError(Exception): """Raised when input fails validation. The ``errors`` attribute maps each offending field to a short reason, suitable for returning to the client as ``{"error": "...", "fields": err.errors}``. """ def __init__(self, errors: dict) -> None: self.errors = errors summary = ", ".join(f"{k}: {v}" for k, v in errors.items()) super().__init__(f"input validation failed ({summary})") # --- Leaf validators: value -> bool. They reject wrong *types* up front, which is # ... 176 more lines ...
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