FKIE_CVE-2014-9636
Vulnerability from fkie_nvd - Published: 2015-02-06 15:59 - Updated: 2026-06-17 00:18
Severity
Summary
unzip 6.0 allows remote attackers to cause a denial of service (out-of-bounds read or write and crash) via an extra field with an uncompressed size smaller than the compressed field size in a zip archive that advertises STORED method compression.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| unzip_project | unzip | 6.0 | |
| canonical | ubuntu_linux | 10.04 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 14.10 | |
| debian | debian_linux | 7.0 | |
| fedoraproject | fedora | 20 | |
| fedoraproject | fedora | 21 |
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"source": "cve@mitre.org"
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},
{
"lang": "es",
"value": "unzip 6.0 permite a atacantes remotos causar una denegaci\u00f3n de servicio (lectura o escritura fuera de rango y ca\u00edda) a trav\u00e9s de un campo extra con un tama\u00f1o comprimido m\u00e1s peque\u00f1o que el tama\u00f1o del campo comprimido en un archivo zip que anuncia la compresi\u00f3n del m\u00e9todo almacenado (STORED)."
}
],
"id": "CVE-2014-9636",
"lastModified": "2026-06-17T00:18:41.323",
"metrics": {
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"baseSeverity": "MEDIUM",
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"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
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"impactScore": 2.9,
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"source": "nvd@nist.gov",
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"published": "2015-02-06T15:59:06.757",
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"url": "http://lists.fedoraproject.org/pipermail/package-announce/2015-January/148792.html"
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"url": "http://seclists.org/oss-sec/2014/q4/496"
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{
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{
"source": "cve@mitre.org",
"url": "http://secunia.com/advisories/62738"
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"url": "http://www.ubuntu.com/usn/USN-2489-1"
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{
"source": "cve@mitre.org",
"url": "https://security.gentoo.org/glsa/201611-01"
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"url": "http://lists.fedoraproject.org/pipermail/package-announce/2015-January/148792.html"
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"vulnStatus": "Modified",
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"value": "CWE-119"
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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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