CVE-2014-0621 (GCVE-0-2014-0621)

Vulnerability from cvelistv5 – Published: 2014-01-08 15:00 – Updated: 2024-08-06 09:20
VLAI
Summary
Multiple cross-site request forgery (CSRF) vulnerabilities in Technicolor (formerly Thomson) TC7200 STD6.01.12 allow remote attackers to hijack the authentication of administrators for requests that (1) perform a factory reset via a request to goform/system/factory, (2) disable advanced options via a request to goform/advanced/options, (3) remove ip-filters via the IpFilterAddressDelete1 parameter to goform/advanced/ip-filters, or (4) remove firewall settings via the cbFirewall parameter to goform/advanced/firewall.
Severity
No CVSS data available.
CWE
  • n/a
Assigner
References
URL Tags
http://www.exploit-db.com/exploits/30667 exploitx_refsource_EXPLOIT-DB
Date Public
2014-01-03 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.