FKIE_CVE-2007-2052
Vulnerability from fkie_nvd - Published: 2007-04-16 22:19 - Updated: 2026-06-16 22:38
Severity
Summary
Off-by-one error in the PyLocale_strxfrm function in Modules/_localemodule.c for Python 2.4 and 2.5 causes an incorrect buffer size to be used for the strxfrm function, which allows context-dependent attackers to read portions of memory via unknown manipulations that trigger a buffer over-read due to missing null termination.
References
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"value": "Off-by-one error in the PyLocale_strxfrm function in Modules/_localemodule.c for Python 2.4 and 2.5 causes an incorrect buffer size to be used for the strxfrm function, which allows context-dependent attackers to read portions of memory via unknown manipulations that trigger a buffer over-read due to missing null termination."
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"value": "Error de superaci\u00f3n de l\u00edmite (off-by-one) en la funci\u00f3n PyLocale_strxfrm de Modules/_localemodule.c para Python 2.4 y 2.5 provoca que se utilice un tama\u00f1o de b\u00fafer incorrecto para la funci\u00f3n strxfrm, lo cual permite a atacantes locales o remotos dependiendo del contexto leer porciones de memoria mediante manipulaciones desconocidas que disparan una lectura fuera de l\u00edmite del b\u00fafer debido a la falta de una terminaci\u00f3n null."
}
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"id": "CVE-2007-2052",
"lastModified": "2026-06-16T22:38:49.477",
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"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:N",
"version": "2.0"
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"vendorComments": [
{
"comment": "Red Hat is aware of this issue and is tracking it via the following bug:\nhttps://bugzilla.redhat.com/bugzilla/show_bug.cgi?id=235093\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:\nhttp://www.redhat.com/security/updates/classification/\n",
"lastModified": "2007-04-19T00:00:00",
"organization": "Red Hat"
}
],
"vulnStatus": "Modified",
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"description": [
{
"lang": "en",
"value": "CWE-193"
}
],
"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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