CVE-2007-0541 (GCVE-0-2007-0541)

Vulnerability from cvelistv5 – Published: 2007-01-29 17:00 – Updated: 2024-08-07 12:19
VLAI
Summary
WordPress allows remote attackers to determine the existence of arbitrary files, and possibly read portions of certain files, via pingback service calls with a source URI that corresponds to a local pathname, which triggers different fault codes for existing and non-existing files, and in certain configurations causes a brief file excerpt to be published as a blog comment.
Severity
No CVSS data available.
CWE
  • n/a
Assigner
References
URL Tags
http://www.securityfocus.com/archive/1/458003/100… mailing-listx_refsource_BUGTRAQ
http://www.securityfocus.com/archive/1/457996/100… mailing-listx_refsource_BUGTRAQ
http://securityreason.com/securityalert/2191 third-party-advisoryx_refsource_SREASON
Date Public
2007-01-24 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.


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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.