CVE-2025-31246 (GCVE-0-2025-31246)

Vulnerability from cvelistv5 – Published: 2025-05-12 21:42 – Updated: 2026-04-02 18:18
VLAI
Summary
The issue was addressed with improved memory handling. This issue is fixed in macOS Sequoia 15.5, macOS Sonoma 14.7.6. Connecting to a malicious AFP server may corrupt kernel memory.
Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-14 03:58 UTC
CWE
  • Connecting to a malicious AFP server may corrupt kernel memory
  • CWE-119 - Improper Restriction of Operations within the Bounds of a Memory Buffer
Assigner
References
Impacted products
Vendor Product Version CPE status
Apple macOS Affected: 0 , < 14.7.6 (custom)
Affected: 0 , < 15.5 (custom)
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

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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.
  • 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.



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.