Rate limit exhaustion is a low-effort, high-impact vector. If your Django API lacks throttling, you're inviting botnets to scrape your data, brute-force your auth, or simply crash your DB with expensive queries. Real-world exploitation involves flooding endpoints like /api/login or /api/search until the worker pool hangs. We fix this by enforcing strict request quotas at the middleware or view level using DRF's throttling engine.

The Vulnerable Pattern

from rest_framework.views import APIView
from rest_framework.response import Response
class SensitiveDataView(APIView):
# VULNERABILITY: No throttle_classes defined.
# Default settings (if not set) allow unlimited hits.
def get(self, request):
# An attacker can script 10,000 requests per second here
# to exhaust DB connections or scrape the entire dataset.
return Response({‘status’: ‘Data delivered’})

The Secure Implementation

The fix leverages Django REST Framework's (DRF) Throttling module. By defining DEFAULT_THROTTLE_CLASSES, we enforce a baseline security posture for all endpoints. For high-cost operations, we use ScopedRateThrottle to apply granular limits. Crucially, in a production environment, you must use a shared cache like Redis (via django-redis) to store throttle counters. Using the default local-memory cache fails in multi-process deployments because counters aren't shared across Gunicorn/Uvicorn workers, allowing attackers to bypass limits by hitting different worker processes.

# settings.py
REST_FRAMEWORK = {
    'DEFAULT_THROTTLE_CLASSES': [
        'rest_framework.throttling.AnonRateThrottle',
        'rest_framework.throttling.UserRateThrottle'
    ],
    'DEFAULT_THROTTLE_RATES': {
        'anon': '10/minute',
        'user': '1000/hour',
        'burst': '5/second'
    }
}
views.py
from rest_framework.throttling import ScopedRateThrottle
class SensitiveDataView(APIView):
throttle_classes = [ScopedRateThrottle]
throttle_scope = ‘burst’
def get(self, request):
    return Response({'status': 'Data secured by rate limiting'})</code></pre>