CVE-2016-8495 (GCVE-0-2016-8495)

Vulnerability from cvelistv5 – Published: 2017-02-13 15:00 – Updated: 2024-10-25 14:40
VLAI
Summary
An improper certificate validation vulnerability in Fortinet FortiManager 5.0.6 through 5.2.7 and 5.4.0 through 5.4.1 allows remote attacker to spoof a trusted entity by using a man-in-the-middle (MITM) attack via the Fortisandbox devices probing feature.
Severity
No CVSS data available.
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-24 20:04 UTC
CWE
  • Credentials exposure
References
URL Tags
http://www.securitytracker.com/id/1037805 vdb-entryx_refsource_SECTRACK
https://fortiguard.com/advisory/FG-IR-16-055 x_refsource_CONFIRM
http://www.securityfocus.com/bid/96157 vdb-entryx_refsource_BID
Impacted products
Vendor Product Version CPE status
Fortinet FortiManager Affected: 5.0.6 to 5.2.7
Affected: 5.4.0 to 5.4.1
guessed Create a notification for this product.
Date Public
2017-02-08 00:00
Show details on NVD website

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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.

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Detection rules are retrieved from Rulezet.

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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.


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