CVE-2026-53877

What it actually does

Django applications using GeoDjango's `django.contrib.gis.gdal.GDALRaster` can over-read an in-memory buffer when a raster is constructed from a Python `bytes` object and the `vsi_buffer` property is accessed. The over-read may expose adjacent process memory to application-level code paths or trigger a segmentation fault, causing worker process crashes or service degradation. Exploitation requires an application code path that accepts attacker-influenced raster bytes and then accesses `vsi_buffer`; the vulnerability is not generally exploitable in Django applications that do not use GeoDjango/GDAL raster processing.

Why it matters to the business

Organizations running affected Django versions with GeoDjango raster-handling features exposed to untrusted input may face limited data disclosure and application instability. The most likely operational impact is degraded availability from crashing web workers or background jobs; in some application designs, sensitive data resident in process memory could be exposed. The issue is not listed in CISA KEV, is not known to be used in ransomware campaigns, and has no reported mature public exploit, but it has been public for 35 days, so exposed raster-processing endpoints should still be prioritized for upgrade.

How an attack unfolds

An attacker first identifies an application feature that accepts geospatial raster content or otherwise passes user-controlled bytes into `django.contrib.gis.gdal.GDALRaster`. The attacker submits a specially crafted byte sequence that causes Django/GDAL to create an in-memory raster with inconsistent or unexpected buffer boundaries. If the vulnerable code later accesses the object's `vsi_buffer` property, Django may read past the intended buffer. Depending on memory layout and error handling, this can return unintended adjacent memory to the application response, logs, downstream processing, or crash the Python worker process with a segmentation fault. Repeated requests can be used to cause intermittent denial of service against affected endpoints or jobs.

How to fix it

Upgrade Django to a fixed supported release: Django 6.0 users should upgrade to 6.0.7 or later, and Django 5.2 users should upgrade to 5.2.16 or later. If running older unsupported Django branches such as 5.0.x, 4.1.x, or 3.2.x, migrate to a supported fixed branch because those versions were not evaluated and may also be affected. If immediate upgrade is not possible, reduce exposure by disabling or restricting features that construct `GDALRaster` from untrusted bytes, validating and size-limiting raster uploads, isolating raster processing in a separate low-privilege worker, and preventing application responses from returning raw `vsi_buffer` output. Apply standard hardening such as worker process supervision, request rate limiting, and memory-safe handling of uploaded geospatial files.

How to confirm the fix worked

Confirm the installed Django version is 6.0.7 or later for the 6.0 branch, or 5.2.16 or later for the 5.2 branch, using package inventory, lockfiles, container images, and runtime checks such as `python -m django --version` or package manager output. Search source code for use of `django.contrib.gis.gdal.GDALRaster`, `GDALRaster(`, and `.vsi_buffer` to determine whether exploitable code paths exist. Verify that raster upload or ingestion endpoints reject malformed files, enforce file size and type limits, and do not expose raw in-memory buffer content. In staging, run regression tests for GeoDjango raster processing and confirm malformed raster inputs fail safely without worker crashes or unexpected response data.

Typical attack path

1. Find a Django application feature that processes uploaded or API-submitted geospatial raster data.
2. Confirm that the feature uses GeoDjango/GDAL raster handling, especially `GDALRaster` constructed from bytes.
3. Submit a malformed or boundary-condition raster payload designed to trigger an in-memory buffer over-read.
4. Trigger application behavior that accesses the `vsi_buffer` property or a code path that indirectly uses it.
5. Observe whether the application leaks unexpected data, returns parsing artifacts, logs abnormal output, or crashes worker processes.
6. Repeat requests to degrade availability if crashes occur.

How to detect it

To assess possible prior exploitation, review web access logs for requests to endpoints that upload, import, preview, transform, or otherwise process geospatial raster files, especially repeated malformed submissions followed by HTTP 500 responses or worker restarts. Examine application logs for exceptions involving GeoDjango, GDAL, `GDALRaster`, or `vsi_buffer`. Review system logs, container runtime logs, process supervisor logs, and APM crash reports for Python worker segmentation faults, `SIGSEGV`, abnormal exits, or repeated restarts around raster-processing requests. Correlate suspicious requests with uploaded file names, MIME types, source IPs, user agents, and timestamps. Because there are no universally reliable network IOCs for this vulnerability, detection should focus on application-specific raster ingestion activity, crashes, and error patterns rather than fixed payload signatures.

• django.contrib.gis.gdal.GDALRaster
• GDALRaster(
• .vsi_buffer
• vsi_buffer
• Segmentation fault
• SIGSEGV
• signal 11
• exited on signal 11
• worker process exited unexpectedly
• GDALRasterException
• GDAL_ERROR
• CPLE_AppDefined
• django.contrib.gis.gdal

What your security team should do

• Inventory Django applications and identify those using GeoDjango/GDAL raster processing.
• Prioritize upgrades for internet-facing applications or APIs that accept geospatial raster uploads or process user-supplied raster bytes.
• Upgrade affected supported branches to Django 6.0.7+ or 5.2.16+; migrate unsupported branches to a supported fixed release.
• Search application code for `GDALRaster` construction from bytes and any access to `.vsi_buffer`; treat these as high-interest exposure points.
• Temporarily disable or restrict untrusted raster ingestion where patching cannot be completed quickly.
• Review web, application, worker, and crash logs for segmentation faults, repeated raster parsing failures, or suspicious upload activity since publication.
• Ensure raster-processing workers run with least privilege and are isolated from secrets or sensitive in-memory data where practical.
• Add monitoring and alerting for repeated 500 errors, worker restarts, SIGSEGV events, and abnormal GDAL/Django raster exceptions.