GSD-2020-7261
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Buffer Overflow via Environment Variables vulnerability in AMSI component in McAfee Endpoint Security (ENS) Prior to 10.7.0 February 2020 Update allows local users to disable Endpoint Security via a carefully crafted user input.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-7261",
"description": "Buffer Overflow via Environment Variables vulnerability in AMSI component in McAfee Endpoint Security (ENS) Prior to 10.7.0 February 2020 Update allows local users to disable Endpoint Security via a carefully crafted user input.",
"id": "GSD-2020-7261"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2020-7261"
],
"details": "Buffer Overflow via Environment Variables vulnerability in AMSI component in McAfee Endpoint Security (ENS) Prior to 10.7.0 February 2020 Update allows local users to disable Endpoint Security via a carefully crafted user input.",
"id": "GSD-2020-7261",
"modified": "2023-12-13T01:21:51.896694Z",
"schema_version": "1.4.0"
}
},
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"DATE_PUBLIC": "2020-04-14T00:00:00.000Z",
"ID": "CVE-2020-7261",
"STATE": "PUBLIC",
"TITLE": "\u202fBuffer overwrite in ENS allowed to bypass AMSI protection"
},
"affects": {
"vendor": {
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{
"product": {
"product_data": [
{
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"version_name": "10.x",
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}
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}
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"impact": {
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"privilegesRequired": "LOW",
"scope": "CHANGED",
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"url": "https://kc.mcafee.com/corporate/index?page=content\u0026id=SB10309"
}
]
},
"source": {
"discovery": "INTERNAL"
}
},
"nvd.nist.gov": {
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}
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},
"impact": {
"baseMetricV2": {
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"cvssV2": {
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"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "NONE",
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"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
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"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "LOW",
"userInteractionRequired": false
},
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"confidentialityImpact": "NONE",
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"scope": "UNCHANGED",
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}
},
"lastModifiedDate": "2020-04-20T20:23Z",
"publishedDate": "2020-04-15T12:15Z"
}
}
}
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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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