CVE-2026-78606 in Kibana
Summary
by MITRE • 09/01/2026
Incorrect Authorization (CWE-863) in Kibana can lead to unauthorized disclosure, modification, and deletion of data via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). Where two authenticated principals originating from different authentication realms share the same username value, one could read, modify, and delete the other's private Elastic AI Assistant Knowledge Base entries.
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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 logic within Kibana, specifically affecting how user identity is resolved across different authentication realms. In complex enterprise environments, it is common to configure multiple authentication providers or realms, such as LDAP, Active Directory, and native users, to accommodate diverse organizational needs. The core technical flaw arises when the system fails to uniquely distinguish between principals from these distinct realms if they share an identical username string. Instead of treating a user named john.doe in Realm A and a user named john.doe in Realm B as two separate entities with independent permissions, Kibana conflates them based solely on the shared identifier. This design oversight effectively bypasses the intended isolation boundaries between different authentication sources, creating a significant security gap that attackers can exploit to escalate privileges or access sensitive data belonging to other users.
This flaw directly enables unauthorized disclosure, modification, and deletion of data through mechanisms classified under CAPEC-1, which describes accessing functionality not properly constrained by Access Control Lists. The impact is most severe in the context of private Elastic AI Assistant Knowledge Base entries. These knowledge bases often contain proprietary business intelligence, sensitive operational procedures, or confidential project details that are intended to be accessible only to specific individuals or teams. When two authenticated principals share a username across different realms, an attacker operating under one realm can authenticate using their credentials and subsequently interact with the system as if they were the user from the other realm. Consequently, the attacker gains full read, write, and delete access to the victim's private AI Assistant data without possessing the victim's actual password or authentication token. This represents a complete breakdown of confidentiality and integrity controls for sensitive organizational assets stored within these knowledge bases.
From an operational perspective, this vulnerability undermines trust in multi-realm authentication setups and exposes organizations to significant risk of intellectual property theft and internal espionage. The ability to silently read confidential information allows adversaries to gather intelligence that could facilitate further attacks or competitive disadvantage. Furthermore, the capacity to modify or delete entries can disrupt business continuity by removing critical reference materials needed for AI-driven assistance tasks. This type of vulnerability is particularly dangerous because it does not require exploiting a buffer overflow or injection flaw; rather, it exploits logical errors in identity management and session handling that are often overlooked during standard penetration testing unless specifically targeted at authentication realm configurations.
To mitigate this risk, organizations must ensure that their Kibana instances are updated to the latest patched versions where this logic error has been corrected by Elastic Security. It is imperative to verify that all users across different authentication realms have unique usernames or that the system explicitly prefixes identities with their source realm to prevent collision. Administrators should also audit existing user configurations to identify any potential username collisions between realms and resolve them immediately. Additionally, implementing strict monitoring for unusual access patterns involving AI Assistant knowledge bases can help detect exploitation attempts in real-time. Regular reviews of authentication provider settings and adherence to the principle of least privilege are essential steps to maintain robust access control integrity within the Elastic Stack ecosystem.