ubuntu-cve-2018-10733
Vulnerability from osv_ubuntu
Published
2018-05-04 17:29
Modified
2026-04-22 07:38
Summary
Details
There is a heap-based buffer over-read in the function ft_font_face_hash of gxps-fonts.c in libgxps through 0.3.0. A crafted input will lead to a remote denial of service attack.
Severity
6.5 (Medium)
N/A (UNKNOWN)
References
| URL | Type | |
|---|---|---|
{
"affected": [
{
"ecosystem_specific": {
"binaries": [
{
"binary_name": "gir1.2-gxps-0.1",
"binary_version": "0.2.3.2-1"
},
{
"binary_name": "libgxps-utils",
"binary_version": "0.2.3.2-1"
},
{
"binary_name": "libgxps2",
"binary_version": "0.2.3.2-1"
}
]
},
"package": {
"ecosystem": "Ubuntu:16.04:LTS",
"name": "libgxps",
"purl": "pkg:deb/ubuntu/libgxps@0.2.3.2-1?arch=source\u0026distro=xenial"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.2.3.2-1"
]
},
{
"ecosystem_specific": {
"binaries": [
{
"binary_name": "gir1.2-gxps-0.1",
"binary_version": "0.3.0-2"
},
{
"binary_name": "libgxps-utils",
"binary_version": "0.3.0-2"
},
{
"binary_name": "libgxps2",
"binary_version": "0.3.0-2"
}
]
},
"package": {
"ecosystem": "Ubuntu:18.04:LTS",
"name": "libgxps",
"purl": "pkg:deb/ubuntu/libgxps@0.3.0-2?arch=source\u0026distro=bionic"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.3.0-1",
"0.3.0-2"
]
}
],
"aliases": [],
"details": "There is a heap-based buffer over-read in the function ft_font_face_hash of gxps-fonts.c in libgxps through 0.3.0. A crafted input will lead to a remote denial of service attack.",
"id": "UBUNTU-CVE-2018-10733",
"modified": "2026-04-22T07:38:36Z",
"published": "2018-05-04T17:29:00Z",
"references": [
{
"type": "REPORT",
"url": "https://ubuntu.com/security/CVE-2018-10733"
},
{
"type": "REPORT",
"url": "https://www.cve.org/CVERecord?id=CVE-2018-10733"
}
],
"related": [],
"schema_version": "1.7.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "low",
"type": "Ubuntu"
}
],
"upstream": [
"CVE-2018-10733"
]
}
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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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