FKIE_CVE-2009-4134
Vulnerability from fkie_nvd - Published: 2010-05-27 19:30 - Updated: 2026-06-16 23:13
Severity
Summary
Buffer underflow in the rgbimg module in Python 2.5 allows remote attackers to cause a denial of service (application crash) via a large ZSIZE value in a black-and-white (aka B/W) RGB image that triggers an invalid pointer dereference.
References
{
"affected": [
{
"affectedData": [
{
"product": "n/a",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "n/a"
}
]
}
],
"source": "secalert@redhat.com"
}
],
"configurations": [
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "Buffer underflow in the rgbimg module in Python 2.5 allows remote attackers to cause a denial of service (application crash) via a large ZSIZE value in a black-and-white (aka B/W) RGB image that triggers an invalid pointer dereference."
},
{
"lang": "es",
"value": "Desbordamiento inferior de b\u00fafer en el m\u00f3dulo rgbimg en Python v2.5 permite a atacantes remotos provocar una denegaci\u00f3n de servicio (ca\u00edda de aplicaci\u00f3n) a trav\u00e9s de un valor ZSIZE grande en una imagen RGB blanco-y-negro (tambi\u00e9n conocido como B/W) que lanza una desreferencia a un puntero no v\u00e1lido."
}
],
"id": "CVE-2009-4134",
"lastModified": "2026-06-16T23:13:06.020",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"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"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
]
},
"published": "2010-05-27T19:30:01.123",
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{
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],
"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Modified",
"weaknesses": [
{
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{
"lang": "en",
"value": "CWE-787"
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"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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