FKIE_CVE-2026-19445
Vulnerability from fkie_nvd - Published: 2026-09-30 17:16 - Updated: 2026-09-30 23:17
Severity
Summary
A remote, unauthenticated TLS client can make a server crash or call
through a freed pointer if its sni_callback assigns a different context to
SSLSocket.context (the documented way to select a certificate per server
name) and nothing else keeps the original ssl.SSLContext alive. Typical
cases are servers that create an SSLContext per connection or replace it
while connections are open; servers that wrap their listening socket with
it are not affected.
Mitigation: keep a reference to every SSLContext that sets sni_callback for
the lifetime of the server. TLS clients are not affected.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"modules": [
"ssl"
],
"product": "CPython",
"repo": "https://github.com/python/cpython",
"vendor": "Python Software Foundation",
"versions": [
{
"lessThan": "3.12.15",
"status": "affected",
"version": "0",
"versionType": "python"
},
{
"lessThan": "3.13.16",
"status": "affected",
"version": "3.13.0",
"versionType": "python"
},
{
"lessThan": "3.14.8",
"status": "affected",
"version": "3.14.0",
"versionType": "python"
},
{
"lessThan": "3.15.0",
"status": "affected",
"version": "3.15.0a1",
"versionType": "python"
}
]
}
],
"source": "cna@python.org"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A remote, unauthenticated TLS client can make a server crash or call\nthrough a freed pointer if its sni_callback assigns a different context to\nSSLSocket.context (the documented way to select a certificate per server\nname) and nothing else keeps the original ssl.SSLContext alive. Typical\ncases are servers that create an SSLContext per connection or replace it\nwhile connections are open; servers that wrap their listening socket with\nit are not affected.\n\n\nMitigation: keep a reference to every SSLContext that sets sni_callback for\nthe lifetime of the server. TLS clients are not affected."
}
],
"id": "CVE-2026-19445",
"lastModified": "2026-09-30T23:17:00.570",
"metrics": {
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "HIGH",
"attackRequirements": "PRESENT",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 9.2,
"baseSeverity": "CRITICAL",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "NOT_DEFINED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"version": "4.0",
"vulnAvailabilityImpact": "LOW",
"vulnConfidentialityImpact": "HIGH",
"vulnIntegrityImpact": "HIGH",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "cna@python.org",
"type": "Secondary"
}
]
},
"published": "2026-09-30T17:16:45.720",
"references": [
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/commit/34a53dce8174da2fceb12fe084a4def02a10053d"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/commit/46133cd57d309652139ada74014aca7665ac552b"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/commit/63fab143d94cafae71850831acfb52041ba44af7"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/commit/cd7e51e7d4563866fbaa1e2521ae69b45daf3698"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/commit/d8717ed01717a9641686e6e6f83f0ab8af235e2c"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/issues/156293"
},
{
"source": "cna@python.org",
"url": "https://github.com/python/cpython/pull/158504"
},
{
"source": "cna@python.org",
"url": "https://mail.python.org/archives/list/security-announce@python.org/thread/QMQIUQB6WGGC3MI7I3WKQXOYOBDSPPS3/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "http://www.openwall.com/lists/oss-security/2026/09/30/17"
}
],
"sourceIdentifier": "cna@python.org",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-416"
}
],
"source": "cna@python.org",
"type": "Secondary"
}
]
}
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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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