FKIE_CVE-2015-5273
Vulnerability from fkie_nvd - Published: 2015-12-07 18:59 - Updated: 2026-06-17 00:28
Severity
Summary
The abrt-action-install-debuginfo-to-abrt-cache help program in Automatic Bug Reporting Tool (ABRT) before 2.7.1 allows local users to write to arbitrary files via a symlink attack on unpacked.cpio in a pre-created directory with a predictable name in /var/tmp.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| redhat | automatic_bug_reporting_tool | * | |
| redhat | enterprise_linux_desktop | 7.0 | |
| redhat | enterprise_linux_hpc_node | 7.0 | |
| redhat | enterprise_linux_server | 7.0 | |
| redhat | enterprise_linux_workstation | 7.0 |
{
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"source": "secalert@redhat.com"
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"value": "The abrt-action-install-debuginfo-to-abrt-cache help program in Automatic Bug Reporting Tool (ABRT) before 2.7.1 allows local users to write to arbitrary files via a symlink attack on unpacked.cpio in a pre-created directory with a predictable name in /var/tmp."
},
{
"lang": "es",
"value": "El programa de ayuda abrt-action-install-debuginfo-to-abrt-cache en Automatic Bug Reporting Tool (ABRT) en versiones anteriores a 2.7.1 permite a usuarios locales escribir archivos arbitrarios a trav\u00e9s de un ataque de un enlace simb\u00f3lico en unpacked.cpio en un directorio creado previamente con un nombre predecible en /var/tmp."
}
],
"id": "CVE-2015-5273",
"lastModified": "2026-06-17T00:28:47.903",
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"baseSeverity": "LOW",
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"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 3.6,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:N/I:P/A:P",
"version": "2.0"
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"impactScore": 4.9,
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"source": "nvd@nist.gov",
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"published": "2015-12-07T18:59:00.137",
"references": [
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"url": "http://lists.fedoraproject.org/pipermail/package-announce/2015-November/172809.html"
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"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Modified",
"weaknesses": [
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"description": [
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"lang": "en",
"value": "CWE-59"
}
],
"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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