CVE-2026-82293 in Kibana
Summary
by MITRE • 09/02/2026
Incorrect Authorization (CWE-863) in the Kibana machine learning feature can lead to unauthorized resource consumption via Exploiting Incorrectly Configured Access Control Security Levels (CAPEC-180). An authenticated user could invoke machine learning functionality beyond their authorization scope, consuming cluster resources they should not be able to reach.
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Analysis
by VulDB Data Team • 09/02/2026
The vulnerability identified as CWE-863 represents a critical failure in access control mechanisms within the Kibana machine learning module. This flaw stems from an incorrect implementation of authorization checks that fail to properly validate whether an authenticated user possesses the necessary privileges for specific operations. In secure systems, authentication verifies identity while authorization determines permitted actions; this defect allows these two processes to become decoupled or improperly enforced. Consequently, users who have been granted limited access rights can bypass restrictions intended to isolate their capabilities from those of administrators or other privileged roles. This misconfiguration creates a pathway for attackers to exploit the system by invoking machine learning functionalities that are restricted to higher-level security tiers, effectively circumventing the designed boundaries of user permissions.
The operational impact of this vulnerability is primarily characterized by unauthorized resource consumption and potential service degradation within the Elasticsearch cluster. Machine learning features in Kibana are computationally intensive, often requiring significant CPU cycles, memory allocation, and storage for data analysis jobs. When an unprivileged or improperly authorized user triggers these functions, they consume resources that should be reserved for legitimate administrative tasks or high-priority analytical workloads. This can lead to a denial of service condition where the cluster becomes sluggish or unresponsive due to excessive load from unauthorized job executions. Furthermore, this abuse may mask malicious activities as normal system operations, making detection more difficult for security teams monitoring standard resource usage patterns.
From an offensive perspective, this vulnerability aligns with CAPEC-180, which describes exploiting incorrectly configured access control security levels. Attackers can leverage this flaw to escalate their effective privileges without needing valid credentials for higher-level roles. By systematically testing various machine learning endpoints and job configurations, a malicious actor can determine the extent of the authorization bypass and then maximize resource consumption or potentially extract sensitive data processed by these jobs if additional vulnerabilities exist in the underlying Elasticsearch engine. This type of attack is particularly dangerous because it does not require traditional exploitation techniques like buffer overflows but rather relies on logical flaws in permission enforcement.
Mitigation strategies must focus on tightening access control policies and enhancing monitoring capabilities. Administrators should review role-based access control (RBAC) configurations to ensure that machine learning features are strictly limited to users with explicit ML-related privileges, such as ml_admin or specific custom roles defined for data science teams. It is essential to apply the principle of least privilege, ensuring that no user has broader permissions than necessary for their job function. Additionally, implementing robust logging and alerting mechanisms can help detect unusual spikes in resource usage associated with machine learning jobs initiated by unexpected users. Regular security audits and penetration testing focused on authorization bypasses are recommended to identify similar logical flaws before they can be exploited in production environments. Keeping Kibana updated to the latest version is also critical, as vendors frequently release patches that address such access control deficiencies.