CVE-2026-53877 djangoproject CVE debrief

Who should care

Users of Django 6.0 before 6.0.7 and 5.2 before 5.2.16 should review and apply patches to prevent potential service degradation or adjacent memory disclosure. This includes operators, platform administrators, vulnerability management teams, and security teams who need to assess the impact of this vulnerability on their systems and take necessary actions.

Technical summary

The `django.contrib.gis.gdal.GDALRaster` class over-reads its in-memory buffer when constructed from a bytes object. This can lead to disclosure of adjacent memory or cause service degradation via a potential segmentation fault when the `vsi_buffer` property is accessed. The vulnerability has been assigned a CVSS score of 6.3, indicating a medium severity. Affected product deployments should be identified, and owners assigned for follow-up.

Defensive priority

Medium priority given the CVSS score of 6.3 and potential for service impact.

Recommended defensive actions

• Review and apply Django patches for versions 6.0 before 6.0.7 and 5.2 before 5.2.16.
• Inventory checks for affected Django versions and configurations.
• Monitoring for potential service degradation or anomalies.
• Exception tracking for `django.contrib.gis.gdal.GDALRaster` usage.
• Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up.
• Review compensating controls for exposed systems while remediation is scheduled and verified.
• Check relevant monitoring, detection, and logs for exposed assets that need extra review.

Evidence notes

The CVE record and NVD entry provide details on the vulnerability in `django.contrib.gis.gdal.GDALRaster`. However, evidence is limited, and further verification is recommended. Affected deployments should be identified, and owners assigned for follow-up. The vulnerability allows for potential disclosure of adjacent memory or service degradation via a segmentation fault when the `vsi_buffer` property is accessed. Defenders should verify affected scope, severity, and vendor guidance.

Sources and references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.