CVE-2026-63261

Published Jul 21, 2026

Last updated 22 days ago

CVSS medium 6.5

Overview

Description
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.
Source
security@elastic.co
NVD status
Analyzed
Products
kibana

Risk scores

CVSS 3.1

Type
Secondary
Base score
6.5
Impact score
3.6
Exploitability score
2.8
Vector string
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Severity
MEDIUM

Weaknesses

security@elastic.co
CWE-400

Social media

Hype score
Not currently trending

  1. 🚨*CVE* CVE-2026-63261 Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send… https://t.co/hTXwQd6Eeu ----- Traducción: CVE-2026-63261 Con… https://t.co/utmtNg

    @infoflowcloud

    21 Jul 2026

    27 Impressions

    0 Retweets

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Configurations

Related CVEs

  1. Missing Authorization (CWE-862) in Kibana can lead to unauthorized cross-space information disclosure via user-supplied input that circumvents space-level access control.•CVE-2026-63262
  2. Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated attacker with low-privilege access can trigger a denial of service condition in Kibana by sending a specially crafted, oversized request payload. Processing this user-supplied input requires resource-intensive memory allocation that can exhaust the available heap memory in the Kibana process, causing it to crash and become unavailable to all users.•CVE-2026-63260
  3. Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to information disclosure via user-supplied identifiers that reference scheduled query result data from Kibana Spaces the requester is not authorized to access.•CVE-2026-63259
  4. Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.•CVE-2026-63145
  5. Missing Authorization (CWE-862) in Kibana can lead to unauthorized information disclosure via Privilege Abuse (CAPEC-122). A user with limited feature privileges can access workflow execution outputs in their Kibana space without the authorization required to do so through the documented API. The accessible data may include sensitive information returned by workflow steps, such as results from connected data sources that the caller would not otherwise be authorized to access.•CVE-2026-63143

References

Sources include official advisories and independent security research.