VAR-202109-1416

Vulnerability from variot - Updated: 2024-08-14 12:47

An out-of-bounds write was addressed with improved input validation. This issue is fixed in iOS 14.7, macOS Big Sur 11.5. Processing a maliciously crafted image may lead to arbitrary code execution. iOS and macOS Big Sur Exists in an out-of-bounds write vulnerability.Information is obtained, information is tampered with, and service operation is interrupted. (DoS) It may be in a state. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Apple macOS. Interaction with the ModelIO library is required to exploit this vulnerability but attack vectors may vary depending on the implementation.The specific flaw exists within the ModelIO framework. Crafted data in an ABC file can trigger a write past the end of an allocated data structure. An attacker can leverage this vulnerability to execute 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

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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

  • 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.
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  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
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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.