FKIE_CVE-2008-1679
Vulnerability from fkie_nvd - Published: 2008-04-22 04:41 - Updated: 2026-06-16 22:52
Severity
Summary
Multiple integer overflows in imageop.c in Python before 2.5.3 allow context-dependent attackers to cause a denial of service (crash) and possibly execute arbitrary code via crafted images that trigger heap-based buffer overflows. NOTE: this issue is due to an incomplete fix for CVE-2007-4965.
References
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"value": "Multiple integer overflows in imageop.c in Python before 2.5.3 allow context-dependent attackers to cause a denial of service (crash) and possibly execute arbitrary code via crafted images that trigger heap-based buffer overflows. NOTE: this issue is due to an incomplete fix for CVE-2007-4965."
},
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"lang": "es",
"value": "M\u00faltiples desbordamientos de entero en imageop.c de Python versiones anteriores a 2.5.3 permiten a atacantes dependientes de contexto provocar una denegaci\u00f3n de servicio (ca\u00edda) y posiblemente ejecutar c\u00f3digo de su elecci\u00f3n a trav\u00e9s de im\u00e1genes manipuladas que disparan desbordamientos de b\u00fafer basado en mont\u00edculo.\r\nNOTA: esta cuesti\u00f3n es debida a una correcci\u00f3n incompleta para CVE-2007-4965."
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"id": "CVE-2008-1679",
"lastModified": "2026-06-16T22:52:15.157",
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"published": "2008-04-22T04:41:00.000",
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"sourceIdentifier": "cve@mitre.org",
"vendorComments": [
{
"comment": "Red Hat is aware of this issue and is tracking it via the following bug: https://bugzilla.redhat.com/bugzilla/show_bug.cgi?id=CVE-2008-1679\n\nThe Red Hat Security Response Team has rated this issue as having low security impact, a future update may address this flaw. More information regarding issue severity can be found here: http://www.redhat.com/security/updates/classification/",
"lastModified": "2008-04-22T00:00:00",
"organization": "Red Hat"
}
],
"vulnStatus": "Modified",
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{
"description": [
{
"lang": "en",
"value": "CWE-190"
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"source": "nvd@nist.gov",
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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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