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CVE-2023-35011 (GCVE-0-2023-35011)

Vulnerability from cvelistv5 – Published: 2023-08-16 22:46 – Updated: 2025-02-13 16:55
VLAI
Title
IBM Cognos Analytics server-side request forgey
Summary
IBM Cognos Analytics 11.1.7, 11.2.0, and 11.2.1 is vulnerable to server-side request forgery (SSRF). This may allow an authenticated attacker to send unauthorized requests from the system, potentially leading to network enumeration or facilitating other attacks. IBM X-Force ID: 257705.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-01 18:13 UTC
CWE
  • CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
Impacted products
Vendor Product Version CPE status
IBM Cognos Analytics Affected: 11.1.7, 11.2.0, 11.2.1
guessed Create a notification for this product.
Show details on NVD website

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  }
}



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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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Detection rules are retrieved from Rulezet.

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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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