GSD-2023-23969
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
In Django 3.2 before 3.2.17, 4.0 before 4.0.9, and 4.1 before 4.1.6, the parsed values of Accept-Language headers are cached in order to avoid repetitive parsing. This leads to a potential denial-of-service vector via excessive memory usage if the raw value of Accept-Language headers is very large.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-23969",
"id": "GSD-2023-23969",
"references": [
"https://advisories.mageia.org/CVE-2023-23969.html",
"https://www.suse.com/security/cve/CVE-2023-23969.html",
"https://ubuntu.com/security/CVE-2023-23969"
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"affected_range": "\u003e=3.2,\u003c3.2.17||\u003e=4.0,\u003c4.0.9||\u003e=4.1,\u003c4.1.6",
"affected_versions": "All versions starting from 3.2 before 3.2.17, all versions starting from 4.0 before 4.0.9, all versions starting from 4.1 before 4.1.6",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"cwe_ids": [
"CWE-1035",
"CWE-770",
"CWE-937"
],
"date": "2023-04-28",
"description": "In Django 3.2 before 3.2.17, 4.0 before 4.0.9, and 4.1 before 4.1.6, the parsed values of Accept-Language headers are cached in order to avoid repetitive parsing. This leads to a potential denial-of-service vector via excessive memory usage if the raw value of Accept-Language headers is very large.",
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"package_slug": "pypi/Django",
"pubdate": "2023-02-01",
"solution": "Upgrade to versions 3.2.17, 4.0.9, 4.1.6 or above.",
"title": "Django contains Uncontrolled Resource Consumption via cached header",
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"impact": {
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6
}
},
"lastModifiedDate": "2023-04-28T05:15Z",
"publishedDate": "2023-02-01T19:15Z"
}
}
}
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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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