FKIE_CVE-2019-17091
Vulnerability from fkie_nvd - Published: 2019-10-02 14:15 - Updated: 2026-06-17 02:23
Severity
Summary
faces/context/PartialViewContextImpl.java in Eclipse Mojarra, as used in Mojarra for Eclipse EE4J before 2.3.10 and Mojarra JavaServer Faces before 2.2.20, allows Reflected XSS because a client window field is mishandled.
References
Impacted products
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "faces/context/PartialViewContextImpl.java in Eclipse Mojarra, as used in Mojarra for Eclipse EE4J before 2.3.10 and Mojarra JavaServer Faces before 2.2.20, allows Reflected XSS because a client window field is mishandled."
},
{
"lang": "es",
"value": "El archivo faces/context/PartialViewContextImpl.java en Eclipse Mojarra, como es usado en Mojarra para Eclipse EE4J versiones anteriores a 2.3.10 y Mojarra JavaServer Faces versiones anteriores a 2.2.20, permite un ataque de tipo XSS Reflejado porque un campo client window es manejado inapropiadamente."
}
],
"id": "CVE-2019-17091",
"lastModified": "2026-06-17T02:23:17.263",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"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"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
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"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
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}
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.7,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2019-10-02T14:15:12.600",
"references": [
{
"source": "cve@mitre.org",
"tags": [
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"Vendor Advisory"
],
"url": "https://bugs.eclipse.org/bugs/show_bug.cgi?id=548244"
},
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"tags": [
"Patch",
"Third Party Advisory"
],
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},
{
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],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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