GSD-2019-7665
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
In elfutils 0.175, a heap-based buffer over-read was discovered in the function elf32_xlatetom in elf32_xlatetom.c in libelf. A crafted ELF input can cause a segmentation fault leading to denial of service (program crash) because ebl_core_note does not reject malformed core file notes.
Aliases
Aliases
{
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"alias": "CVE-2019-7665",
"description": "In elfutils 0.175, a heap-based buffer over-read was discovered in the function elf32_xlatetom in elf32_xlatetom.c in libelf. A crafted ELF input can cause a segmentation fault leading to denial of service (program crash) because ebl_core_note does not reject malformed core file notes.",
"id": "GSD-2019-7665",
"references": [
"https://www.suse.com/security/cve/CVE-2019-7665.html",
"https://access.redhat.com/errata/RHBA-2020:0547",
"https://access.redhat.com/errata/RHSA-2019:3575",
"https://access.redhat.com/errata/RHSA-2019:2197",
"https://ubuntu.com/security/CVE-2019-7665",
"https://advisories.mageia.org/CVE-2019-7665.html",
"https://security.archlinux.org/CVE-2019-7665",
"https://linux.oracle.com/cve/CVE-2019-7665.html"
]
},
"gsd": {
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"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"modified": "2023-12-13T01:23:46.967967Z",
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
"version": "2.0"
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"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
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"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
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},
"lastModifiedDate": "2021-11-30T19:53Z",
"publishedDate": "2019-02-09T16:29Z"
}
}
}
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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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