VAR-202109-1356
Vulnerability from variot - Updated: 2024-08-14 12:06An out-of-bounds write was addressed with improved input validation. This issue is fixed in iOS 14.5 and iPadOS 14.5, watchOS 7.4, Security Update 2021-003 Catalina, tvOS 14.5, macOS Big Sur 11.3. Processing a maliciously crafted image may lead to arbitrary code execution. This vulnerability allows remote attackers to disclose sensitive information on affected installations of Apple macOS. Interaction with the ImageIO library is required to exploit this vulnerability but attack vectors may vary depending on the implementation.The specific flaw exists within the ImageIO framework. Crafted data in a PCT file can trigger a read past the end of an allocated data structure. An attacker can leverage this in conjunction with other vulnerabilities to execute arbitrary code in the context of the current process. 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. A buffer error vulnerability exists in ImageIO for Apple MacOS due to a boundary error in ImageIO's handling of untrusted input. The vulnerability could allow a remote attacker to exploit the vulnerability to compromise a vulnerable system. The following products and versions are affected: macOS 10.15 19A583, 10.15 19A602, 10.15 19A603, 10.15.1 19B88, 10.15.2 19C57, 10.15.3 19D76, 10.15.4 19E266, 10.15.4 5, 159E287, 10.19.15 19F101, 10.15.6 19G73, 10.15.6 19G2021
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.