OESA-2026-2298 (CVE-2026-44431)
Vulnerability from osv_openeuler – Published: 2026-05-15 11:11 – Updated: 2026-08-06 11:11 – Source website
VLAI
Summary
python-urllib3 security update
Details
HTTP library with thread-safe connection pooling, file post support, sanity friendly, and more.
Security Fix(es):
urllib3 is an HTTP client library for Python. From 1.23 to before 2.7.0, cross-origin redirects followed from the low-level API via ProxyManager.connection_from_url().urlopen(..., assert_same_host=False) still forward these sensitive headers. This vulnerability is fixed in 2.7.0.(CVE-2026-44431)
Severity
5.9 (Medium)
References
| URL | Type | |
|---|---|---|
{
"affected": [
{
"ecosystem_specific": {
"noarch": [
"python3-urllib3-1.26.18-8.oe2403sp1.noarch.rpm"
],
"src": [
"python-urllib3-1.26.18-8.oe2403sp1.src.rpm"
]
},
"package": {
"ecosystem": "openEuler:24.03-LTS-SP1",
"name": "python-urllib3",
"purl": "pkg:rpm/openEuler/python-urllib3\u0026distro=openEuler-24.03-LTS-SP1"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.26.18-8.oe2403sp1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"severity": "Medium"
},
"details": "HTTP library with thread-safe connection pooling, file post support, sanity friendly, and more.\r\n\r\nSecurity Fix(es):\n\nurllib3 is an HTTP client library for Python. From 1.23 to before 2.7.0, cross-origin redirects followed from the low-level API via ProxyManager.connection_from_url().urlopen(..., assert_same_host=False) still forward these sensitive headers. This vulnerability is fixed in 2.7.0.(CVE-2026-44431)",
"id": "OESA-2026-2298",
"modified": "2026-08-06T11:11:16Z",
"published": "2026-05-15T11:11:16Z",
"references": [
{
"type": "ADVISORY",
"url": "https://www.openeuler.org/zh/security/security-bulletins/detail/?id=openEuler-SA-2026-2298"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44431"
}
],
"schema_version": "1.7.2",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:N",
"type": "CVSS_V3"
}
],
"summary": "python-urllib3 security update",
"upstream": [
"CVE-2026-44431"
]
}
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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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