CVE-2018-1353 (GCVE-0-2018-1353)

Vulnerability from cvelistv5 – Published: 2018-09-05 13:00 – Updated: 2024-10-25 14:08
VLAI
Summary
An information disclosure vulnerability in Fortinet FortiManager 6.0.1 and below versions allows a standard user with adom assignment read the interface settings of vdoms unrelated to the assigned adom.
Severity
No CVSS data available.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-23 14:00 UTC
CWE
  • Improper Access Control
Assigner
References
URL Tags
https://fortiguard.com/advisory/FG-IR-18-016 x_refsource_CONFIRM
Impacted products
Vendor Product Version CPE status
Fortinet, Inc. FortiManager Affected: 6.0.1 and below
guessed Create a notification for this product.
Date Public
2018-08-27 00:00
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.