GSD-2022-36359
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
An issue was discovered in the HTTP FileResponse class in Django 3.2 before 3.2.15 and 4.0 before 4.0.7. An application is vulnerable to a reflected file download (RFD) attack that sets the Content-Disposition header of a FileResponse when the filename is derived from user-supplied input.
Aliases
Aliases
{
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"alias": "CVE-2022-36359",
"description": "An issue was discovered in the HTTP FileResponse class in Django 3.2 before 3.2.15 and 4.0 before 4.0.7. An application is vulnerable to a reflected file download (RFD) attack that sets the Content-Disposition header of a FileResponse when the filename is derived from user-supplied input.",
"id": "GSD-2022-36359",
"references": [
"https://advisories.mageia.org/CVE-2022-36359.html",
"https://www.suse.com/security/cve/CVE-2022-36359.html",
"https://ubuntu.com/security/CVE-2022-36359",
"https://www.debian.org/security/2022/dsa-5254",
"https://security.archlinux.org/CVE-2022-36359"
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},
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"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"modified": "2023-12-13T01:19:21.735085Z",
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"affected_range": "\u003e=3.2,\u003c3.2.15||\u003e=4.0,\u003c4.0.7",
"affected_versions": "All versions starting from 3.2 before 3.2.15, all versions starting from 4.0 before 4.0.7",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"cwe_ids": [
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"CWE-494",
"CWE-937"
],
"date": "2023-04-28",
"description": "An issue was discovered in the HTTP FileResponse class in Django 3.2 before 3.2.15 and 4.0 before 4.0.7. An application is vulnerable to a reflected file download (RFD) attack that sets the Content-Disposition header of a FileResponse when the filename is derived from user-supplied input.",
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"package_slug": "pypi/Django",
"pubdate": "2022-08-03",
"solution": "Upgrade to versions 3.2.15, 4.0.7 or above.",
"title": "Download of Code Without Integrity Check",
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"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2023-04-28T05:15Z",
"publishedDate": "2022-08-03T14:15Z"
}
}
}
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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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