GHSA-JX2M-7CW3-3CF8
Vulnerability from github – Published: 2026-08-13 21:36 – Updated: 2026-08-13 21:36
VLAI
Details
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.
Severity
6.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-72674"
],
"database_specific": {
"cwe_ids": [
"CWE-770"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-13T20:17:28Z",
"severity": "MODERATE"
},
"details": "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.",
"id": "GHSA-jx2m-7cw3-3cf8",
"modified": "2026-08-13T21:36:10Z",
"published": "2026-08-13T21:36:10Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-72674"
},
{
"type": "WEB",
"url": "https://discuss.elastic.co/t/kibana-9-3-8-9-4-4-security-update-esa-2026-91/389533"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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