GHSA-2m8g-3cmr-wg3w

Summary

While investigating Django REST Framework's request parsing behavior, I identified that DRF's high-level request.data parsing appears to bypass Django's configured DATA_UPLOAD_MAX_MEMORY_SIZE protection for application/json and application/x-www-form-urlencoded request bodies.

In the tested configurations, Django correctly raises RequestDataTooBig when applications access request.body or Django's native request.POST, but DRF successfully parses the same oversized payloads through request.data.

This behavior appears to occur because DRF passes the underlying HttpRequest object directly to parsers, which consume the request stream through Django's lower-level streaming interface rather than the guarded request.body path.

I am reporting this privately because I am unsure whether this behavior is considered part of DRF's intended security boundary, but it appears to bypass a documented Django request-size protection for common DRF request parsing paths and may have availability implications.

What I Verified

I verified the behavior locally using the following combinations:

• Django 6.0.7 + DRF 3.17.1 → Affected
• Django 6.0.7 + DRF current upstream main → Affected

For both versions, the observed behavior was:

Django request.body
→ RequestDataTooBig

Django request.POST (application/x-www-form-urlencoded)
→ RequestDataTooBig

Django request.read()
→ Reads the entire oversized request body

DRF request.data
→ Successfully parses oversized JSON and urlencoded request bodies

I also confirmed that:

• multipart/form-data remains protected because DRF delegates multipart parsing to Django's multipart parser.
• The behavior reproduces on both direct WSGI and ASGI servers without a reverse proxy or external request-size middleware.

Technical Details

The relevant execution flow is:

APIView

↓

rest_framework.request.Request

↓

request.data

↓

Request._load_data_and_files()

↓

Request._parse()

↓

Request._load_stream()

↓

self._stream = self._request

↓

JSONParser.parse(...)
or
FormParser.parse(...)

↓

stream.read() / json.load(...)

The important implementation detail is that DRF assigns the original Django HttpRequest object as the parser stream.

Unlike request.body and Django's native form parsing, consuming the stream through HttpRequest.read() does not trigger Django's RequestDataTooBig protection.

As a result, DRF's built-in parsers successfully consume oversized request bodies that Django itself would reject through its higher-level request interfaces.

Reproduction Steps

Environment

Python 3.13

Django 6.0.7

Django REST Framework 3.17.1 (also reproduced on current upstream main)

Configure:

DATA_UPLOAD_MAX_MEMORY_SIZE = 10

Create a simple DRF API view:

from rest_framework.views import APIView
from rest_framework.response import Response

class DemoView(APIView):
    def post(self, request):
        return Response(request.data)

Start the application.

Send an oversized JSON request:

POST /demo
Content-Type: application/json
Content-Length: >10 bytes

Example:

{
  "value": "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA..."
}

Observed:

HTTP 200

JSON successfully parsed

Now compare against:

request.body

Observed:

RequestDataTooBig

Likewise, compare against:

request.POST

using

application/x-www-form-urlencoded

Observed:

RequestDataTooBig

This demonstrates different enforcement depending on which request API is used.

Root Cause

Django documents HttpRequest.read() as a streaming interface.

DRF exposes request.data as the primary high-level request parsing API.

Currently, DRF forwards the raw Django request stream directly to parsers before any request-size validation equivalent to Django's request.body path occurs.

Consequently:

• JSONParser
• FormParser

fully consume oversized request bodies despite Django's configured request-size limit.

Security Impact

This does not appear to introduce:

• Authentication bypass
• Authorization bypass
• Remote code execution
• Information disclosure
• Integrity compromise

However, it may reduce the effectiveness of deployments relying on Django's DATA_UPLOAD_MAX_MEMORY_SIZE to limit request-body resource consumption.

Potential consequences include:

• Additional memory allocation during JSON parsing
• Additional CPU usage while decoding large JSON payloads
• Increased resource consumption when handling oversized request bodies
• Reduced effectiveness of Django's configured request-size protection for DRF endpoints using request.data

The practical impact depends on deployment configuration, including:

• upstream request-size limits
• reverse proxy configuration
• authentication
• rate limiting
• endpoint exposure

Memory Observations

During local testing I observed successful parsing of oversized request bodies despite the configured limit.

Representative measurements showed significantly increased memory allocation while parsing large JSON and urlencoded payloads.

I intentionally did not perform destructive concurrency testing or attempt to exhaust system resources.

Scope

Confirmed affected:

• application/json
• application/x-www-form-urlencoded

Confirmed not affected:

• multipart/form-data

Suggested Fix Direction

One possible approach would be for DRF to enforce Django's configured DATA_UPLOAD_MAX_MEMORY_SIZE before handing the raw request stream to parsers that fully materialize request bodies in memory.

This would preserve Django's configured request-size protection for the common request.data API without requiring broader changes to Django's documented streaming interface.

Versions Tested

Affected:

• Django 6.0.7 + DRF 3.17.1
• Django 6.0.7 + DRF current upstream main

I did not perform a complete historical version bisect.

Disclosure

I have not publicly disclosed this behavior.

I am submitting it privately in accordance with the project's security policy because I am unsure whether maintainers consider this part of DRF's intended security boundary.

Note:

Thank you for taking the time to review this report.

If you determine that this behavior should be addressed, I would be happy to help investigate further, develop a fix, add regression tests, and submit a patch if you'd find that helpful.

I have experience as a Python/Django software engineer, security researcher, and open-source contributor, and I'd be glad to contribute if you think that would be useful.