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Combined Vulnerability List

Inadequate Rate Limiting in Multi-Process Environments with LocMemCache

  • Vulnerability Name: Inadequate Rate Limiting in Multi-Process Environments with LocMemCache
  • Description:
    1. An attacker targets a Django application that uses django-ratelimit for rate limiting.
    2. The application is configured to use django.core.cache.backends.locmem.LocMemCache as the cache backend.
    3. The Django application is deployed in a multi-process environment, such as using WSGI servers like Gunicorn or uWSGI with multiple worker processes, or ASGI servers with multiple processes.
    4. The attacker sends requests to a rate-limited endpoint.
    5. Because LocMemCache is not shared between processes, each process maintains its own independent rate limit counters.
    6. If the attacker's requests are distributed across multiple processes (which can happen due to load balancing or request distribution in multi-process environments), the rate limit is applied per process, not globally.
    7. As a result, the attacker can send more requests in total than the intended global rate limit by effectively resetting the rate limit counter in each process.
  • Impact: Rate limits can be circumvented, allowing attackers to bypass intended restrictions. This can lead to abuse of application resources, such as excessive API calls, brute-force attacks, or other actions that rate limiting is meant to prevent. This undermines the security functionality of the rate limiter.
  • Vulnerability Rank: High
  • Currently Implemented Mitigations:
    • The project includes a system check (django_ratelimit.E003) that flags django.core.cache.backends.locmem.LocMemCache as a broken backend because it is not a shared cache.
    • This system check generates an error during Django's check command, warning developers about the unsuitable cache backend.
    • However, this is only a warning and does not prevent the use of LocMemCache. Developers can silence this check, as demonstrated in the provided test_settings.py file.
  • Missing Mitigations:
    • Prevent or strongly discourage the use of LocMemCache or other non-shared cache backends in production environments. This could involve:
      • Enhancing the system check to be a critical error that cannot be easily silenced, especially in production settings.
      • Adding runtime warnings or exceptions when LocMemCache is detected in a multi-process environment.
    • Improve documentation to clearly highlight the limitations of LocMemCache in multi-process environments and strongly recommend shared cache backends like Memcached or Redis for production deployments.
  • Preconditions:
    • Django application is deployed in a multi-process environment.
    • django-ratelimit is configured to use django.core.cache.backends.locmem.LocMemCache.
  • Source Code Analysis:
    1. /code/django_ratelimit/checks.py: The check_caches function explicitly identifies django.core.cache.backends.locmem.LocMemCache in KNOWN_BROKEN_CACHE_BACKENDS with the reason CACHE_NOT_SHARED. This results in a checks.Error with id='django_ratelimit.E003'.
    2. /code/test_settings.py: The SILENCED_SYSTEM_CHECKS = ['django_ratelimit.E003', 'django_ratelimit.W001'] setting in the test configuration silences this error, indicating awareness of the issue but no active prevention in the library itself beyond the system check.
    3. /code/django_ratelimit/core.py: The is_ratelimited and get_usage functions rely on Django's cache framework to store and retrieve rate limit counters. When LocMemCache is configured, each process operates on its own isolated in-memory cache.
    4. In multi-process environments, requests can be routed to different processes. Each process will independently manage rate limits based on its local LocMemCache. This process-local rate limiting fails to provide a global rate limit across the entire application instance.
  • Security Test Case:
    1. Setup:
      • Deploy a Django application using django-ratelimit.
      • Configure settings.py to use LocMemCache: 
        RATELIMIT_USE_CACHE = 'default'
CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
        'LOCATION': 'ratelimit-tests',
    }
}
      • Configure a view to be rate-limited, for example: 
        from django_ratelimit.decorators import ratelimit
from django.http import HttpResponse

@ratelimit(key='ip', rate='2/m')
def my_view(request):
    return HttpResponse("View Accessed")
      • Deploy the Django application with a multi-process server like Gunicorn (e.g., gunicorn -w 2 your_project.wsgi).
    2. Attack Simulation:
      • From an attacker's machine, use a tool like curl or a script to send HTTP requests to the rate-limited view.
      • Send 3 or more requests within a minute from the same IP address. Try to distribute requests in time to potentially hit different processes if possible but even rapid requests can be distributed by OS scheduler.
      • Observe the HTTP responses.
    3. Expected Result (Vulnerable Case - LocMemCache):
      • All requests (including the 3rd and subsequent requests within the minute) will likely return a successful "View Accessed" response (HTTP 200). This indicates that the rate limit of 2 requests per minute is not being enforced globally across processes and is being bypassed.
    4. Mitigation Test:
      • Modify settings.py to use a shared cache backend like Redis: 
        RATELIMIT_USE_CACHE = 'default'
CACHES = {
    'default': {
        'BACKEND': 'django_redis.cache.RedisCache',
        'LOCATION': 'redis://127.0.0.1:6379/1', # Replace with your Redis connection details
        'OPTIONS': {
            'CLIENT_CLASS': 'django_redis.client.DefaultClient',
        }
    }
}
        (Ensure django-redis is installed and Redis server is running).
      • Redeploy the Django application.
      • Repeat step 2 (send 3 or more requests within a minute).
    5. Expected Result (Mitigated Case - Redis):
      • The first two requests will return a successful "View Accessed" response (HTTP 200).
      • The third and subsequent requests within the minute should be blocked by the rate limiter, and will likely return a Ratelimited exception (e.g., HTTP 403 if default exception handling is in place, or a custom response if middleware is configured). This confirms that the rate limit is now being enforced globally.

Rate Limit Bypass via Cache Failure (Fail Open Behavior)

  • Vulnerability Name: Rate Limit Bypass via Cache Failure (Fail Open Behavior)
  • Description:
    1. An external attacker targets an endpoint that is protected with rate-limiting logic (e.g., a login or password-reset endpoint decorated with @ratelimit).
    2. The attacker induces cache backend unreachability or unresponsiveness (e.g., network issues, flooding, misconfigurations).
    3. When a request is processed, get_usage attempts to use the cache (via cache.add or cache.incr). Cache call fails or returns "no value".
    4. The code catches the error and, if RATELIMIT_FAIL_OPEN=True, returns None.
    5. is_ratelimited interprets None usage as "not ratelimited", permitting the request.
    6. The attacker sends many requests, bypassing rate limits, enabling brute-force or abuse.
  • Impact: Bypassing rate limiting allows brute-force attacks, resource exhaustion, and abuse of protected functionality. Severe security and operational consequences in scenarios requiring strict rate limits.
  • Vulnerability Rank: High
  • Currently Implemented Mitigations:
    • get_usage catches cache connection errors (e.g., socket.gaierror) and respects RATELIMIT_FAIL_OPEN setting.
    • "Fail open" approach prevents blocking legitimate traffic during cache issues.
  • Missing Mitigations:
    • No built-in "fail closed" alternative for enforcing rate limiting during cache unreachability.
    • No fallback mechanisms (local counters, alerting) for cache connectivity issues.
    • Lack of enhanced monitoring and automatic stricter rate limiting during cache failures.
  • Preconditions:
    • Application deployed with network-reachable caching backend susceptible to disruption.
    • RATELIMIT_FAIL_OPEN=True is configured (or default "fail open" behavior).
    • Attacker can induce cache connectivity failures or influence cache behavior.
  • Source Code Analysis:
    • django_ratelimit/core.py, get_usage function: 
      try:
    added = cache.add(cache_key, initial_value, period + EXPIRATION_FUDGE)
except socket.gaierror:  # for redis
    added = False
      try:
    count = cache.incr(cache_key)
except ValueError:
    pass
      if count is None or count is False:
    if getattr(settings, 'RATELIMIT_FAIL_OPEN', False):
        return None
    return { 'count': 0, ... 'should_limit': True, ... }
    • Returning None from get_usage bypasses rate limit in is_ratelimited.
  • Security Test Case:
    1. Setup:
      • Deploy application with django-ratelimit, rate-limited login view (@ratelimit(key='ip', rate='1/m')).
      • Configure caching backend (Redis/memcached), set RATELIMIT_FAIL_OPEN=True.
    2. Simulate Cache Failure:
      • Make cache backend unreachable:
        • (a) Point RATELIMIT_USE_CACHE to non-existent server.
        • (b) Network partition/firewall blocking cache access.
    3. Execution:
      • Send excessive HTTP requests from external client to protected endpoint.
    4. Verification:
      • Observe endpoint does not throttle requests after exceeding rate limit.
      • Confirm cache operations fail and get_usage returns None, bypassing rate limiting.
    5. Conclusion:
      • Attacker-induced cache failures bypass rate-limiting protections, verifying vulnerability.

No High-Rank Vulnerabilities Found

  • Vulnerability Name: No High-Rank Vulnerabilities Found
  • Description: After a thorough review of the django-ratelimit project, considering the perspective of an external attacker targeting a publicly available instance, and applying the exclusion and inclusion criteria specified, no vulnerabilities of high or critical rank were identified that are inherent to the library itself and exploitable in its intended usage. Potential misconfigurations or misuse of the library in specific application contexts might lead to ineffective rate limiting, but these are not considered vulnerabilities within the scope of this analysis of the django-ratelimit project's code and design.
  • Impact: N/A
  • Vulnerability Rank: N/A
  • Currently Implemented Mitigations:
    • CodeQL static analysis.
    • Comprehensive test suite.
    • Configurable cache backend with problematic backend checks.
    • Default SHA256 for cache key hashing.
  • Missing Mitigations: N/A
  • Preconditions: N/A
  • Source Code Analysis: N/A
  • Security Test Case: N/A