CVE-2026-87890 (GCVE-0-2026-87890)
Vulnerability from cvelistv5 – Published: 2026-10-06 13:35 – Updated: 2026-10-06 14:06
VLAI
EPSS
VEX
Title
Potential request forgery via spatial lookup byte values
Summary
An issue was discovered in Django 6.1 before 6.1.2, 6.0 before 6.0.9, and 5.2 before 5.2.18.
An incomplete fix for CVE-2026-15307 in Django spatial lookups allows an attacker who can supply `bytes` values to cause the Django process to make network requests via a crafted VRT document referencing an external raster source.
Earlier, unsupported Django series (such as 5.1.x, 5.0.x, and 4.2.x) were not evaluated and may also be affected.
Django would like to thank sicksec for reporting this issue.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-06 14:06 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
7 references
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| djangoproject | Django |
Affected:
6.1 , < 6.1.2
(python)
Unaffected: 6.1.2 (python) Affected: 6.0 , < 6.0.9 (python) Unaffected: 6.0.9 (python) Affected: 5.2 , < 5.2.18 (python) Unaffected: 5.2.18 (python) |
Date Public
2026-10-06 08:00
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"vendor-advisory"
],
"url": "https://www.djangoproject.com/weblog/2026/oct/06/security-releases/"
}
],
"source": {
"discovery": "EXTERNAL"
},
"timeline": [
{
"lang": "en",
"time": "2026-08-15T04:09:55.000Z",
"value": "Initial report received."
},
{
"lang": "en",
"time": "2026-08-27T04:10:18.000Z",
"value": "Vulnerability confirmed."
},
{
"lang": "en",
"time": "2026-10-06T08:00:00.000Z",
"value": "Security release issued."
}
],
"title": "Potential request forgery via spatial lookup byte values",
"x_generator": {
"engine": "cvelib 1.8.0"
}
}
},
"cveMetadata": {
"assignerOrgId": "6a34fbeb-21d4-45e7-8e0a-62b95bc12c92",
"assignerShortName": "DSF",
"cveId": "CVE-2026-87890",
"datePublished": "2026-10-06T13:35:37.964Z",
"dateReserved": "2026-09-09T14:50:25.689Z",
"dateUpdated": "2026-10-06T14:06:56.731Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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