CVE-2020-15935 (GCVE-0-2020-15935)

Vulnerability from cvelistv5 – Published: 2021-11-02 19:00 – Updated: 2024-10-25 13:43
VLAI
Summary
A cleartext storage of sensitive information in GUI in FortiADC versions 5.4.3 and below, 6.0.0 and below may allow a remote authenticated attacker to retrieve some sensitive information such as users LDAP passwords and RADIUS shared secret by deobfuscating the passwords entry fields.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-23 13:57 UTC
CWE
  • Information disclosure
References
URL Tags
https://fortiguard.com/advisory/FG-IR-20-044 x_refsource_CONFIRM
Impacted products
Vendor Product Version CPE status
Fortinet Fortinet FortiADC Affected: FortiADC 5.4.3 6.0.0
guessed Create a notification for this product.
Show details on NVD website

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      "dataVersion": "5.1"
    }
  }
}



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Tags
Taxonomy of the tags.


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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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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.


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