CVE-2021-30961 (GCVE-0-2021-30961)

Vulnerability from cvelistv5 – Published: 2021-08-24 18:51 – Updated: 2024-08-03 22:48
VLAI
Summary
A buffer overflow issue was addressed with improved memory handling. This issue is fixed in Security Update 2021-008 Catalina, macOS Big Sur 11.6.2. Parsing a maliciously crafted audio file may lead to disclosure of user information.
Severity
5.5 (Medium)
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N
CWE
  • Parsing a maliciously crafted audio file may lead to disclosure of user information
Assigner
References
Impacted products
Vendor Product Version CPE status
Apple macOS Affected: unspecified , < 11.6 (custom)
guessed Create a notification for this product.
Apple macOS Affected: unspecified , < 2021 (custom)
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

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Tags
Taxonomy of the tags.





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.


Detection rules are retrieved from Rulezet.


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.