FKIE_CVE-2009-0031
Vulnerability from fkie_nvd - Published: 2009-01-21 02:30 - Updated: 2026-06-16 23:04
Severity
Summary
Memory leak in the keyctl_join_session_keyring function (security/keys/keyctl.c) in Linux kernel 2.6.29-rc2 and earlier allows local users to cause a denial of service (kernel memory consumption) via unknown vectors related to a "missing kfree."
References
Impacted products
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{
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{
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{
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{
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{
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{
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{
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],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Memory leak in the keyctl_join_session_keyring function (security/keys/keyctl.c) in Linux kernel 2.6.29-rc2 and earlier allows local users to cause a denial of service (kernel memory consumption) via unknown vectors related to a \"missing kfree.\""
},
{
"lang": "es",
"value": "Fuga de memoria en la funci\u00f3n keyctl_join_session_keyring (security/keys/keyctl.c) en el kernel de Linux 2.6.29-rc2 y anteriores permite a usuarios locales provocar una denegaci\u00f3n de servicio(agotamiento de memoria del n\u00facleo) a trav\u00e9s de vectores desconocidos relacionados con \"kfree perdido.\""
}
],
"id": "CVE-2009-0031",
"lastModified": "2026-06-16T23:04:07.543",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": true,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "COMPLETE",
"baseScore": 4.9,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:C",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 6.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
]
},
"published": "2009-01-21T02:30:00.313",
"references": [
{
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"url": "http://git2.kernel.org/?p=linux/kernel/git/torvalds/linux-2.6.git%3Ba=commit%3Bh=0d54ee1c7850a954026deec4cd4885f331da35cc"
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"sourceIdentifier": "secalert@redhat.com",
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"weaknesses": [
{
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{
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"value": "CWE-399"
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],
"source": "nvd@nist.gov",
"type": "Primary"
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]
}
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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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