FKIE_CVE-2005-1212
Vulnerability from fkie_nvd - Published: 2005-06-14 04:00 - Updated: 2026-06-16 22:12
Severity
Summary
Buffer overflow in Microsoft Step-by-Step Interactive Training (orun32.exe) allows remote attackers to execute arbitrary code via a bookmark link file (.cbo, cbl, or .cbm extension) with a long User field.
References
Impacted products
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Buffer overflow in Microsoft Step-by-Step Interactive Training (orun32.exe) allows remote attackers to execute arbitrary code via a bookmark link file (.cbo, cbl, or .cbm extension) with a long User field."
},
{
"lang": "es",
"value": "El desbordamiento de b\u00fafer en Microsoft Step-by-Step Interactive Training (orun32.exe) permite a los atacantes remotos ejecutar c\u00f3digo arbitrario a trav\u00e9s de un archivo de enlace de marcadores (extensi\u00f3n.cbo, cbl o.cbm) con un campo de usuario largo."
}
],
"id": "CVE-2005-1212",
"lastModified": "2026-06-16T22:12:39.450",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "HIGH",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": true,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
]
},
"published": "2005-06-14T04:00:00.000",
"references": [
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"source": "secure@microsoft.com",
"tags": [
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"url": "http://idefense.com/application/poi/display?id=262\u0026type=vulnerabilities\u0026flashstatus=true"
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{
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},
{
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},
{
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"url": "https://docs.microsoft.com/en-us/security-updates/securitybulletins/2005/ms05-031"
},
{
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],
"sourceIdentifier": "secure@microsoft.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "NVD-CWE-Other"
}
],
"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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