CVE-2015-1451 (GCVE-0-2015-1451)

Vulnerability from cvelistv5 – Published: 2015-02-02 16:00 – Updated: 2024-08-06 04:40
VLAI
Summary
Multiple cross-site scripting (XSS) vulnerabilities in Fortinet FortiOS 5.0 Patch 7 build 4457 allow remote authenticated users to inject arbitrary web script or HTML via the (1) WTP Name or (2) WTP Active Software Version field in a CAPWAP Join request.
Severity
No CVSS data available.
CWE
  • n/a
Assigner
References
URL Tags
http://seclists.org/fulldisclosure/2015/Jan/125 mailing-listx_refsource_FULLDISC
http://www.security-assessment.com/files/document… x_refsource_MISC
http://www.fortiguard.com/advisory/FG-IR-15-002/ x_refsource_CONFIRM
http://secunia.com/advisories/61661 third-party-advisoryx_refsource_SECUNIA
http://www.securityfocus.com/bid/72383 vdb-entryx_refsource_BID
Date Public
2015-01-29 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.


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.