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CVE-2020-36610 (GCVE-0-2020-36610)

Vulnerability from cvelistv5 – Published: 2022-12-08 00:00 – Updated: 2025-04-15 13:07
VLAI
Title
annyshow DuxCMS cross-site request forgery
Summary
A vulnerability was found in annyshow DuxCMS 2.1. It has been declared as problematic. This vulnerability affects unknown code. The manipulation leads to cross-site request forgery. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-215116.
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-14 16:55 UTC
CWE
  • CWE-863 - Incorrect Authorization -> CWE-862 Missing Authorization -> CWE-352 Cross-Site Request Forgery
Impacted products
Vendor Product Version CPE status
annyshow DuxCMS Affected: 2.1
guessed Create a notification for this product.
Show details on NVD website

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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.

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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.


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