FKIE_CVE-2019-9621
Vulnerability from fkie_nvd - Published: 2019-04-30 18:29 - Updated: 2026-06-17 02:44
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
7.5 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N
7.5 (High) - CVSS:3.1/
Summary
Zimbra Collaboration Suite before 8.6 patch 13, 8.7.x before 8.7.11 patch 10, and 8.8.x before 8.8.10 patch 7 or 8.8.x before 8.8.11 patch 3 allows SSRF via the ProxyServlet component.
References
Impacted products
{
"affected": [
{
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"vendor": "n/a",
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"source": "cve@mitre.org"
}
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"cisaExploitAdd": "2025-07-07",
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],
"negate": false,
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]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Zimbra Collaboration Suite before 8.6 patch 13, 8.7.x before 8.7.11 patch 10, and 8.8.x before 8.8.10 patch 7 or 8.8.x before 8.8.11 patch 3 allows SSRF via the ProxyServlet component."
},
{
"lang": "es",
"value": "Zimbra Collaboration Suite anterior de la versi\u00f3n 8.6 path 13, versi\u00f3n 8.7.x anterior de la 8.7.11 path 10, y versi\u00f3n 8.8.x anterior de la 8.8.10 path 7 u versi\u00f3n 8.8.x anterior de la 8.8.11 path 3, permite vulnerabilidad de tipo SSRF por medio del componente ProxyServlet."
}
],
"id": "CVE-2019-9621",
"lastModified": "2026-06-17T02:44:03.280",
"metrics": {
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{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 5.0,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:N",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
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"source": "nvd@nist.gov",
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}
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"scope": "UNCHANGED",
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},
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"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
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{
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"role": "CISA Coordinator",
"timestamp": "2025-07-17T03:55:42.480607Z",
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}
}
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},
"published": "2019-04-30T18:29:08.633",
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{
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"tags": [
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],
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog?field_cve=CVE-2019-9621"
}
],
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"vulnStatus": "Analyzed",
"weaknesses": [
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"value": "CWE-918"
}
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{
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}
]
}
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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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