Description

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.

Executive Summary

Technical Details

Mitigations

References

Additional Metadata

{
  "score": "4.3",
  "pubDate": "2026-09-02T15:17:43.157Z",
  "pubdate": "2026-09-02T15:17:43.157Z",
  "executiveSummary": "The Kibana machine learning feature is susceptible to an Incorrect Authorization vulnerability (CWE-863), facilitating unauthorized resource consumption via CAPEC-180.\nThis security flaw resides within the Kibana platform, allowing an authenticated user to invoke machine learning operations that exceed their defined authorization scope.\nThe primary impact is the unauthorized utilization of cluster resources, which can lead to denial-of-service conditions or resource depletion for legitimate administrative tasks.\nExploitation requires the attacker to possess authenticated access to the Kibana instance, though they do not require administrative privileges for the targeted machine learning features.\nThe vulnerability highlights a failure in the application's access control enforcement logic, where security levels are not consistently validated during the dispatch of specific machine learning function calls.\nOrganizations face significant operational risk, as unauthorized users can inadvertently or maliciously overload infrastructure capacity, undermining cluster stability and performance.",
  "technicalDetails": "The vulnerability is rooted in an improper authorization implementation within the Kibana machine learning subsystem. Specifically, the application fails to adequately validate a user's authorization scope against the requested machine learning resources during the request lifecycle.\nWhen an authenticated user interacts with machine learning endpoints, the system is expected to perform a granular check to verify if the user possesses the requisite security permissions to trigger specific jobs or data feed operations. Due to the failure in CWE-863 compliance, the backend logic incorrectly assumes that the user context is pre-validated or allows requests to proceed based on insufficient privilege levels.\nThe exploitation process follows a path consistent with CAPEC-180: Exploiting Incorrectly Configured Access Control Security Levels. An attacker, already authenticated within the environment, crafts a malicious API request targeting machine learning features that are ostensibly restricted. Because the authorization layer fails to enforce mandatory access control checks at the function level, the request is processed by the underlying Elasticsearch cluster on behalf of the user.\nStep-by-step attack flow: 1. The attacker authenticates to the Kibana instance using standard credentials. 2. The attacker identifies internal machine learning API endpoints that they should be restricted from accessing. 3. The attacker issues a specifically formatted payload to these endpoints. 4. The Kibana backend fails to reject the request, allowing the interaction to reach the machine learning controller. 5. The machine learning component executes the command, consuming significant CPU, memory, or storage resources on the Elasticsearch cluster.\nThe technical failure originates in the component responsible for reconciling user roles with machine learning functional requirements. By bypassing these checks, an attacker can initiate computationally expensive training tasks, anomaly detection jobs, or data analysis feeds. This leads to an immediate post-exploitation impact characterized by resource starvation. If an attacker triggers multiple concurrent, heavy-load machine learning jobs, they can effectively perform a denial-of-service attack against the cluster, slowing down or crashing critical data indexing and query operations for legitimate users.\nThe vulnerability persists because the authorization logic acts as a permissive gateway rather than a strictly validated gatekeeper for these specific administrative functionalities, failing to verify the authorization bounds prior to resource allocation."
}