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CVE-2018-4875 (GCVE-0-2018-4875)

Vulnerability from cvelistv5 – Published: 2018-02-27 05:00 – Updated: 2024-08-05 05:18
VLAI
Summary
Adobe Experience Manager versions 6.1 and 6.0 are vulnerable to a reflected cross-site scripting vulnerability related to the handling of malicious content embedded in image files uploaded to the DAM.
Severity
6.1 (Medium)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
CWE
  • Reflected cross-site scripting
Assigner
References
URL Tags
http://www.securityfocus.com/bid/102991 vdb-entryx_refsource_BID
https://helpx.adobe.com/security/products/experie… x_refsource_CONFIRM
http://www.securitytracker.com/id/1040365 vdb-entryx_refsource_SECTRACK
Impacted products
Vendor Product Version CPE status
n/a Adobe Experience Manager 6.1, 6.0 Affected: Adobe Experience Manager 6.1, 6.0
Date Public
2018-02-26 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.