CVE-2026-114138 in Django REST Framework
Summary
by MITRE • 10/21/2026
Django REST framework is a toolkit for building Web APIs. Prior to 3.18.2, DefaultContentNegotiation.get_accept_list in rest_framework/negotiation.py accepts an unbounded number and aggregate size of Accept tokens, while select_renderer in that file repeatedly invokes _MediaType parsing from rest_framework/utils/mediatypes.py for each attacker-controlled token, every renderer, and forced 406 renegotiation. A remote unauthenticated client can send many distinct media types containing large quoted parameter values with numerous semicolons, causing Django's parameter parser and DRF's repeated parsing structure to consume disproportionate CPU. Requests to any reachable endpoint can pin synchronous workers for prolonged periods, and concurrent requests can make the service unavailable until the requests stop. This issue is fixed in version 3.18.2.
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Analysis
by VulDB Data Team • 10/21/2026
The vulnerability identified in Django REST framework prior to version 3.18.2 represents a significant resource exhaustion risk stemming from inefficient input validation within the content negotiation subsystem. The core technical flaw resides in the DefaultContentNegotiation.get_accept_list method located in rest_framework/negotiation.py, which fails to impose limits on either the quantity or the aggregate size of Accept tokens provided by clients. This lack of constraint allows remote unauthenticated actors to craft HTTP requests containing an excessive number of distinct media types, each potentially including large quoted parameter values with numerous semicolons. The operational mechanism exploits a nested parsing inefficiency where select_renderer repeatedly invokes _MediaType parsing from rest_framework/utils/mediatypes.py for every single token against every configured renderer. This creates a multiplicative effect in computational complexity, as the system performs redundant and exhaustive string processing operations that scale poorly with input size rather than handling them efficiently or rejecting oversized inputs early in the request lifecycle.
The impact of this vulnerability is severe, primarily manifesting as a denial-of-service condition through CPU exhaustion and worker thread blocking. Because Django REST framework often operates within synchronous WSGI servers such as Gunicorn or uWSGI, each incoming malicious request consumes a dedicated worker process for an extended period while it attempts to parse the malformed Accept header. This prolonged processing time effectively pins these workers, preventing them from handling legitimate traffic. In environments with limited concurrency limits, even a small number of concurrent requests can exhaust all available worker threads, rendering the entire service unavailable to other users until the malicious requests cease or are forcibly terminated by an external load balancer or timeout mechanism. This behavior aligns closely with CWE-400, which describes uncontrolled resource consumption, and maps directly to MITRE ATT&CK technique T1496, representing Resource Hijacking where attackers consume resources to degrade service availability for legitimate users.
Mitigation strategies must prioritize upgrading the Django REST framework installation immediately to version 3.18.2 or later, as this release includes specific patches that enforce bounds on Accept header parsing and optimize the negotiation logic to prevent excessive CPU consumption. For organizations unable to patch instantly due to dependency constraints, defensive measures should include configuring reverse proxies such as Nginx or Apache to inspect and truncate overly long HTTP headers before they reach the application server. Additionally, implementing rate limiting at the network edge can help mitigate the impact of concurrent attack attempts by throttling requests from single sources that exhibit patterns consistent with this exploitation vector. Monitoring for unusually high CPU usage correlated with specific API endpoints may also serve as an early detection mechanism for ongoing abuse until permanent remediation is applied.