CVE-2026-72674: CWE-770 Allocation of Resources Without Limits or Throttling in Elastic Kibana
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). A user-supplied list of document fields accepted by the Kibana Playground for RAG feature was neither bounded in length nor de-duplicated before it was used to assemble the response for each matching document. A single crafted request could therefore make Kibana build a response far larger than the data it was derived from, and the resulting processing and memory pressure exhausts the resources of the Kibana instance.
AI Analysis
Technical Summary
This vulnerability (CWE-770) in Kibana arises because the user-supplied list of document fields accepted by the Kibana Playground for the RAG feature is neither bounded in length nor de-duplicated before assembling the response. An attacker can send a specially crafted request that causes Kibana to generate a response far larger than the original data, leading to excessive processing and memory consumption that exhausts the Kibana instance's resources, resulting in denial of service.
Potential Impact
Successful exploitation leads to denial of service by exhausting the processing and memory resources of the affected Kibana instance. There is no impact on confidentiality or integrity reported. The CVSS score is 6.5 (medium), reflecting network attack vector, low attack complexity, required privileges, no user interaction, and impact limited to availability.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. No official fix or temporary workaround is currently documented. Until a patch is available, limit access to Kibana instances and monitor for unusual resource consumption related to the Playground RAG feature.
CVE-2026-72674: CWE-770 Allocation of Resources Without Limits or Throttling in Elastic Kibana
Description
Allocation of Resources Without Limits or Throttling (CWE-770) in Kibana can lead to a denial of service via Excessive Allocation (CAPEC-130). A user-supplied list of document fields accepted by the Kibana Playground for RAG feature was neither bounded in length nor de-duplicated before it was used to assemble the response for each matching document. A single crafted request could therefore make Kibana build a response far larger than the data it was derived from, and the resulting processing and memory pressure exhausts the resources of the Kibana instance.
CVSS v3.1
Score 6.5medium
Affected software
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Weaknesses
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
This vulnerability (CWE-770) in Kibana arises because the user-supplied list of document fields accepted by the Kibana Playground for the RAG feature is neither bounded in length nor de-duplicated before assembling the response. An attacker can send a specially crafted request that causes Kibana to generate a response far larger than the original data, leading to excessive processing and memory consumption that exhausts the Kibana instance's resources, resulting in denial of service.
Potential Impact
Successful exploitation leads to denial of service by exhausting the processing and memory resources of the affected Kibana instance. There is no impact on confidentiality or integrity reported. The CVSS score is 6.5 (medium), reflecting network attack vector, low attack complexity, required privileges, no user interaction, and impact limited to availability.
Mitigation Recommendations
Patch status is not yet confirmed — check the vendor advisory for current remediation guidance. No official fix or temporary workaround is currently documented. Until a patch is available, limit access to Kibana instances and monitor for unusual resource consumption related to the Playground RAG feature.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- elastic
- Date Reserved
- 2026-08-10T11:17:49.704Z
- Cvss Version
- 3.1
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 6a7e1a84bf8831d539b18382
Added to database: 08/13/2026, 19:27:00 UTC
Last enriched: 08/13/2026, 19:43:54 UTC
Last updated: 08/13/2026, 21:46:55 UTC
Views: 3
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