CVE-2024-0204 (GCVE-0-2024-0204)

Vulnerability from cvelistv5 – Published: 2024-01-22 18:05 – Updated: 2025-05-30 14:22
VLAI
Title
Authentication Bypass in GoAnywhere MFT
Summary
Authentication bypass in Fortra's GoAnywhere MFT prior to 7.4.1 allows an unauthorized user to create an admin user via the administration portal.
Severity
9.8 (Critical)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: yes Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-08 15:41 UTC
CWE
  • CWE-425 - Direct Request ('Forced Browsing')
Assigner
References
Impacted products
Vendor Product Version
Fortra GoAnywhere MFT Affected: 6.0.1 , < 7.4.1 (semver)
Create a notification for this product.
Credits
Mohammed Eldeeb & Islam Elrfai, Spark Engineering Consultants
Show details on NVD website
To clipboard 

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Taxonomy of the tags.





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.