GSD-2013-1443
Vulnerability from gsd - Updated: 2023-12-13 01:22Details
The authentication framework (django.contrib.auth) in Django 1.4.x before 1.4.8, 1.5.x before 1.5.4, and 1.6.x before 1.6 beta 4 allows remote attackers to cause a denial of service (CPU consumption) via a long password which is then hashed.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2013-1443",
"description": "The authentication framework (django.contrib.auth) in Django 1.4.x before 1.4.8, 1.5.x before 1.5.4, and 1.6.x before 1.6 beta 4 allows remote attackers to cause a denial of service (CPU consumption) via a long password which is then hashed.",
"id": "GSD-2013-1443",
"references": [
"https://www.suse.com/security/cve/CVE-2013-1443.html",
"https://www.debian.org/security/2013/dsa-2758",
"https://advisories.mageia.org/CVE-2013-1443.html"
]
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"id": "GSD-2013-1443",
"modified": "2023-12-13T01:22:21.010859Z",
"schema_version": "1.4.0"
}
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"name": "[django-developers] 20130914 Set a reasonable upper bound on password length",
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{
"affected_range": "\u003e=1.4,\u003c1.4.8||\u003e=1.5,\u003c1.5.4",
"affected_versions": "All versions starting from 1.4 before 1.4.8, all versions starting from 1.5 before 1.5.4",
"cvss_v2": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"cwe_ids": [
"CWE-1035",
"CWE-287",
"CWE-937"
],
"date": "2014-01-27",
"description": "The authentication framework (django.contrib.auth) computes the hash of a password each time a user attempts to log in, no matter the length of the password. Thus, a remote attacker can cause a denial of service (CPU consumption) by repeatedly submitting long passwords.",
"fixed_versions": [
"1.4.8",
"1.5.4"
],
"identifier": "CVE-2013-1443",
"identifiers": [
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],
"package_slug": "pypi/Django",
"pubdate": "2013-09-23",
"solution": "Upgrade to latest",
"title": "DOS via large passwords",
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"impact": {
"baseMetricV2": {
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
}
},
"lastModifiedDate": "2014-01-28T04:51Z",
"publishedDate": "2013-09-23T20:55Z"
}
}
}
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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.
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- 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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