FKIE_CVE-2019-16935
Vulnerability from fkie_nvd - Published: 2019-09-28 02:15 - Updated: 2026-06-17 02:22
Severity
Summary
The documentation XML-RPC server in Python through 2.7.16, 3.x through 3.6.9, and 3.7.x through 3.7.4 has XSS via the server_title field. This occurs in Lib/DocXMLRPCServer.py in Python 2.x, and in Lib/xmlrpc/server.py in Python 3.x. If set_server_title is called with untrusted input, arbitrary JavaScript can be delivered to clients that visit the http URL for this server.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| python | python | * | |
| python | python | * | |
| python | python | * | |
| python | python | * | |
| debian | debian_linux | 9.0 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 16.04 | |
| canonical | ubuntu_linux | 18.04 | |
| canonical | ubuntu_linux | 19.04 |
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The documentation XML-RPC server in Python through 2.7.16, 3.x through 3.6.9, and 3.7.x through 3.7.4 has XSS via the server_title field. This occurs in Lib/DocXMLRPCServer.py in Python 2.x, and in Lib/xmlrpc/server.py in Python 3.x. If set_server_title is called with untrusted input, arbitrary JavaScript can be delivered to clients that visit the http URL for this server."
},
{
"lang": "es",
"value": "La documentaci\u00f3n del servidor XML-RPC en Python versiones hasta 2.7.16, versiones 3.x hasta 3.6.9 y versiones 3.7.x hasta 3.7.4, presenta una vulnerabilidad de tipo XSS por medio del campo server_title. Esto ocurre en Lib/DocXMLRPCServer.py en Python versi\u00f3n 2.x, y en Lib/xmlrpc/server.py en Python versi\u00f3n 3.x. Si es llamada set_server_title con una entrada no confiable, puede ser entregado un JavaScript arbitrario a los clientes que visitan la URL http para este servidor."
}
],
"id": "CVE-2019-16935",
"lastModified": "2026-06-17T02:22:59.773",
"metrics": {
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"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
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"impactScore": 2.9,
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"published": "2019-09-28T02:15:10.463",
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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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