CVE-2014-9472 (GCVE-0-2014-9472)
Vulnerability from cvelistv5 – Published: 2015-03-09 14:00 – Updated: 2024-08-06 13:47
VLAI
EPSS
VEX
Summary
The email gateway in RT (aka Request Tracker) 3.0.0 through 4.x before 4.0.23 and 4.2.x before 4.2.10 allows remote attackers to cause a denial of service (CPU and disk consumption) via a crafted email.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
5 references
| URL | Tags |
|---|---|
| http://blog.bestpractical.com/2015/02/security-vu… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/72832 | vdb-entryx_refsource_BID |
| http://www.debian.org/security/2015/dsa-3176 | vendor-advisoryx_refsource_DEBIAN |
| http://lists.fedoraproject.org/pipermail/package-… | vendor-advisoryx_refsource_FEDORA |
| http://lists.fedoraproject.org/pipermail/package-… | vendor-advisoryx_refsource_FEDORA |
Date Public
2015-02-26 00:00
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"url": "http://lists.fedoraproject.org/pipermail/package-announce/2015-April/154213.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "http://lists.fedoraproject.org/pipermail/package-announce/2015-March/154047.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "http://www.debian.org/security/2015/dsa-3176"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory",
"VDB Entry"
],
"url": "http://www.securityfocus.com/bid/72832"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-399"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2025-11-21T12:21:33+00:00",
"cve": "CVE-2014-9472",
"id": "CVE-2014-9472",
"initial_release_date": "2014-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "rt: denial of service flaw in email gateway",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2014/cve-2014-9472.json",
"version": "3"
}
}
}
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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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