Rate Limiting Bypass in Flask with Hmac Signatures

Rate limiting is a common control to protect APIs from abuse. In Flask applications, developers sometimes use HMAC signatures to authenticate requests, ensuring integrity and source authenticity. However, relying solely on HMAC for request validation while having weak or missing rate limiting on the endpoint can create a Rate Limiting Bypass. An attacker who discovers or guesses a valid HMAC key (or uses a compromised key) can generate arbitrarily many signed requests, consuming server resources and evading rate-based protections.

The vulnerability occurs when rate limiting is applied before signature verification or not applied to the path used for authentication. For example, if an endpoint accepts signed requests and enforces rate limits on unsigned, unauthenticated paths but the signed route does not enforce limits, an attacker can route traffic through the signed path to exhaust quotas. Additionally, if the server uses a static or predictable key, an attacker can replay or forge requests at scale. This combination allows an attacker to bypass intended throttling, potentially leading to denial of service for legitimate users or enabling brute-force or scraping activities.

Consider an endpoint that processes financial transfers identified by an idempotency key. If the route uses HMAC to validate request origin but does not enforce per-client rate limits, an authenticated attacker can flood the endpoint with valid signed requests. Even if the application enforces global rate limits, poor key management or lack of per-key tracking can allow a single compromised key to dominate traffic. The risk is compounded when the signed route also exposes sensitive operations that would otherwise be protected by stricter controls.

In practice, scanning such an API with middleBrick can surface this class of issue under the BFLA/Privilege Escalation and Rate Limiting checks, highlighting mismatches between authentication and throttling scope. The scanner does not attempt to exploit the flaw but identifies that a signed route lacks sufficient rate controls and may expose an unauthenticated attack surface through predictable or missing limits.

Developers should ensure that rate limiting is applied after successful authentication and covers all authorized paths, with per-key tracking where keys are used. Keys must be rotated regularly and stored securely, avoiding hard-coded values in source code. MiddleBrick’s OpenAPI/Swagger analysis can help detect routes where authentication and rate limiting scopes are inconsistent, providing prioritized findings with remediation guidance.

To remediate Rate Limiting Bypass when using HMAC signatures in Flask, enforce rate limits on the authenticated route itself and tie limits to the identity derived from the signature. Below is a minimal, realistic example showing how to structure request validation and rate limiting using a shared secret and the hmac module, with Flask-compatible patterns.

import time
import hmac
import hashlib
import json
from flask import Flask, request, jsonify

app = Flask(__name__)

# Example shared secret stored securely (e.g., from environment/secrets)
SHARED_SECRET = b'super-secret-key-2025'

# Simple in-memory store for per-client rate tracking; use Redis in production
rate_store = {}

def verify_hmac_signature(data, signature):
    """Verify HMAC-SHA256 signature."""
    mac = hmac.new(SHARED_SECRET, data, hashlib.sha256)
    return hmac.compare_digest(mac.hexdigest(), signature)

@app.route('/api/transfer', methods=['POST'])
def transfer():
    payload = request.get_data()
    signature = request.headers.get('X-Signature')
    if not signature:
        return jsonify({'error': 'missing signature'}), 401
    if not verify_hmac_signature(payload, signature):
        return jsonify({'error': 'invalid signature'}), 401

    # Identify client by a key included in the payload or derived from signature context
    # For this example, assume client_id is part of the JSON body
    try:
        body = json.loads(payload)
        client_id = body.get('client_id')
    except (json.JSONDecodeError, TypeError):
        return jsonify({'error': 'invalid payload'}), 400

    if not client_id:
        return jsonify({'error': 'missing client_id'}), 400

    # Enforce rate limit per client (e.g., 10 requests per 60 seconds)
    now = time.time()
    window = 60
    limit = 10
    client_bucket = rate_store.setdefault(client_id, [])
    # Remove outdated timestamps
    client_bucket[:] = [t for t in client_bucket if now - t < window]
    if len(client_bucket) >= limit:
        return jsonify({'error': 'rate limit exceeded'}), 429
    client_bucket.append(now)

    # Process the transfer (business logic omitted)
    return jsonify({'status': 'accepted'}), 200

if __name__ == '__main__':
    app.run(debug=False)

The example demonstrates signature verification before processing and per-client rate tracking. In production, replace the in-memory store with a distributed cache like Redis to ensure consistency across workers and instances. Always use hmac.compare_digest to avoid timing attacks, and rotate SHARED_SECRET periodically. Scope rate limits to authenticated identities so that signed routes are not excluded from controls.

Using middleBrick’s CLI (middlebrick scan <url>) or GitHub Action, you can automatically detect mismatches where authenticated endpoints lack per-client limits. The tool’s findings include severity and remediation guidance, helping you prioritize fixes such as adding per-key tracking and ensuring limits apply after successful HMAC validation.