CVE-2025-36140 (GCVE-0-2025-36140)

Vulnerability from cvelistv5 – Published: 2025-12-08 22:11 – Updated: 2025-12-09 16:04
VLAI
Title
IBM watsonx.data Denial of Service
Summary
IBM watsonx.data 2.2 through 2.2.1 could allow an authenticated user to cause a denial of service through ingestion pods due to improper allocation of resources without limits.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-09 15:24 UTC
CWE
  • CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
References
URL Tags
https://www.ibm.com/support/pages/node/7253932 vendor-advisorypatch
Impacted products
Vendor Product Version CPE status
IBM watsonx.data Affected: 2.2 , ≤ 2.2.1 (semver)
    cpe:2.3:a:ibm:watsonx.data:2.2:*:*:*:*:*:*:*
    cpe:2.3:a:ibm:watsonx.data:2.2.1:*:*:*:*:*:*:*
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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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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