GHSA-GWVQ-RGQF-993F
Vulnerability from github – Published: 2022-05-17 00:13 – Updated: 2024-10-25 20:47
VLAI
Summary
python-keystoneclient vulnerable to context confusion in Keystone auth_token middleware
Details
A context confusion vulnerability was identified in Keystone auth_token middleware (shipped in python-keystoneclient) before 0.7.0. By doing repeated requests, with sufficient load on the target system, an authenticated user may in certain situations assume another authenticated user's complete identity and multi-tenant authorizations, potentially resulting in a privilege escalation. Note that it is related to a bad interaction between eventlet and python-memcached that should be avoided if the calling process already monkey-patches "thread" to use eventlet. Only keystone middleware setups using auth_token with memcache are vulnerable.
Severity
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 0.6.0"
},
"package": {
"ecosystem": "PyPI",
"name": "python-keystoneclient"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.7.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2014-0105"
],
"database_specific": {
"cwe_ids": [
"CWE-522"
],
"github_reviewed": true,
"github_reviewed_at": "2023-08-03T23:09:34Z",
"nvd_published_at": "2014-04-15T14:55:00Z",
"severity": "LOW"
},
"details": "A context confusion vulnerability was identified in Keystone auth_token middleware (shipped in python-keystoneclient) before 0.7.0. By doing repeated requests, with sufficient load on the target system, an authenticated user may in certain situations assume another authenticated user\u0027s complete identity and multi-tenant authorizations, potentially resulting in a privilege escalation. Note that it is related to a bad interaction between eventlet and python-memcached that should be avoided if the calling process already monkey-patches \"thread\" to use eventlet. Only keystone middleware setups using auth_token with memcache are vulnerable.",
"id": "GHSA-gwvq-rgqf-993f",
"modified": "2024-10-25T20:47:18Z",
"published": "2022-05-17T00:13:54Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2014-0105"
},
{
"type": "WEB",
"url": "https://bugs.launchpad.net/python-keystoneclient/+bug/1282865"
},
{
"type": "PACKAGE",
"url": "https://github.com/openstack/python-keystoneclient"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/python-keystoneclient/PYSEC-2014-70.yaml"
},
{
"type": "WEB",
"url": "https://review.opendev.org/c/openstack/python-keystoneclient/+/81078"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2014-0382.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2014-0409.html"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2014/03/27/4"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:L/I:L/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:N/SC:L/SI:L/SA:L",
"type": "CVSS_V4"
}
],
"summary": "python-keystoneclient vulnerable to context confusion in Keystone auth_token middleware"
}
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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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