CVE-2015-7032 (GCVE-0-2015-7032)

Vulnerability from cvelistv5 – Published: 2015-10-18 19:00 – Updated: 2024-08-06 07:36
VLAI
Summary
The Apple iWork application before 2.6 for iOS, Apple Keynote before 6.6, Apple Pages before 5.6, and Apple Numbers before 3.6 allow remote attackers to obtain sensitive information via a crafted document.
Severity
No CVSS data available.
CWE
  • n/a
Assigner
apple CNA under the mitre root
CNA scorecard E 37/100 over 619 records in the last 180 days details
References
URL Tags
http://www.securitytracker.com/id/1033823 vdb-entryx_refsource_SECTRACK
http://www.securitytracker.com/id/1033825 vdb-entryx_refsource_SECTRACK
http://www.securitytracker.com/id/1033826 vdb-entryx_refsource_SECTRACK
http://lists.apple.com/archives/security-announce… vendor-advisoryx_refsource_APPLE
https://support.apple.com/HT205373 x_refsource_CONFIRM
Date Public
2015-10-15 00:00
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.