VAR-202109-1308
Vulnerability from variot - Updated: 2023-12-18 11:32This issue was addressed with improved handling of file metadata. This issue is fixed in macOS Big Sur 11.3. A malicious application may bypass Gatekeeper checks. macOS Big Sur Contains a vulnerability in the integrity verification flaw of the downloaded code.Information may be tampered with. Pillow is a Python-based image processing library. There is currently no information about this vulnerability, please feel free to follow CNNVD or manufacturer announcements. Apple macOS is a set of dedicated operating systems developed by Apple Corporation for Mac computers. An input validation error vulnerability exists in macOS due to insufficient validation of user-supplied input within installer components when processing metadata. The following products and versions are affected: macOS: 11.0 20A2411, 11.0.1 20B29, 11.0.1 20B50, 11.1 20C69, 11.2 20D64, 11.2.1 20D74, 11.2.1 20D75, 11.2.2 20D80, 11.2.3 2
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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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