GSD-2021-25372
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
An improper boundary check in DSP driver prior to SMR Mar-2021 Release 1 allows out of bounds memory access.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-25372",
"description": "An improper boundary check in DSP driver prior to SMR Mar-2021 Release 1 allows out of bounds memory access.",
"id": "GSD-2021-25372"
},
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"remediation": "unknown",
"reportConfidence": "confirmed",
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],
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"id": "GSD-2021-25372",
"modified": "2023-12-13T01:23:21.010386Z",
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"lastModifiedDate": "2021-04-01T19:06Z",
"publishedDate": "2021-03-26T19:15Z"
}
}
}
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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
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- Confirmed: The vulnerability has been validated from an analyst's perspective.
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- 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.
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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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