GSD-2021-37852
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
ESET products for Windows allows untrusted process to impersonate the client of a pipe, which can be leveraged by attacker to escalate privileges in the context of NT AUTHORITY\SYSTEM.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-37852",
"description": "ESET products for Windows allows untrusted process to impersonate the client of a pipe, which can be leveraged by attacker to escalate privileges in the context of NT AUTHORITY\\SYSTEM.",
"id": "GSD-2021-37852"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2021-37852"
],
"details": "ESET products for Windows allows untrusted process to impersonate the client of a pipe, which can be leveraged by attacker to escalate privileges in the context of NT AUTHORITY\\SYSTEM.",
"id": "GSD-2021-37852",
"modified": "2023-12-13T01:23:10.098941Z",
"schema_version": "1.4.0"
}
},
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"DATE_PUBLIC": "2022-01-31T09:00:00.000Z",
"ID": "CVE-2021-37852",
"STATE": "PUBLIC",
"TITLE": "LPE in ESET products for Windows"
},
"affects": {
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{
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{
"product_name": "ESET NOD32 Antivirus",
"version": {
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}
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},
{
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},
{
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"version_value": "15.0.18.0"
}
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}
},
{
"product_name": "ESET Endpoint Antivirus for Windows",
"version": {
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"version_name": "6.6.2046.0",
"version_value": "9.0.2032.4"
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},
{
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},
{
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},
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},
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},
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"credit": [
{
"lang": "eng",
"value": "Michael DePlante (@izobashi) of Trend Micro\u0027s Zero Day Initiative"
}
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"name": "https://support.eset.com/en/ca8223-local-privilege-escalation-vulnerability-fixed-in-eset-products-for-windows",
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"lang": "eng",
"value": "The attack surface can also be eliminated by disabling the Enable advanced scanning via AMSI option in ESET products\u00e2\u20ac\u2122 Advanced setup.\n\nHowever, ESET strongly recommends performing an upgrade to a fixed product version and only applying this workaround when the upgrade is not possible for an important reason."
}
]
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"lastModifiedDate": "2022-02-11T19:35Z",
"publishedDate": "2022-02-09T06:15Z"
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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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