CVE-2026-87017 in Open WebUI
Summary
by MITRE • 09/09/2026
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform. From 0.7.0 until 0.11.1, the built-in knowledge search tool passed the caller's readable knowledge identifiers through a metadata filter, but the search methods in eleven shipped vector backends ignored that filter. An authenticated user on an affected backend could enumerate the identifiers, names, and descriptions of inaccessible knowledge bases from the shared collection, although the associated document text remained in separate collections. This issue is fixed in version 0.11.1.
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Analysis
by VulDB Data Team • 09/09/2026
The vulnerability identified within Open WebUI versions ranging from 0.7.0 to 0.11.1 represents a significant failure in access control enforcement, specifically categorized under CWE-284 Improper Access Control and aligned with the MITRE ATT&CK technique T1069 Permission Groups Discovery. This flaw stems from an architectural inconsistency between the application layer's metadata filtering logic and the underlying vector database implementations used for knowledge retrieval. The system is designed to support multiple shared collections of knowledge bases, where each base contains sensitive documents that must be restricted based on user permissions. When a user initiates a search or query against these knowledge repositories, the application constructs a filter containing specific identifiers associated with the accessible knowledge bases and passes this metadata to the vector backend for execution.
The core technical flaw lies in the fact that while the Open WebUI application layer correctly generates filters intended to restrict results to only those documents belonging to authorized knowledge bases, eleven of the supported vector backends completely ignore these metadata filters during their search operations. This disregard means that the query is executed against the entire dataset within a shared collection rather than being scoped down by the provided identifiers. Consequently, an authenticated user can perform searches or list operations that return results from knowledge bases they do not have permission to access. Although the actual content of the documents remains isolated in separate collections and is not directly exposed through this specific vector search mechanism, the metadata associated with these inaccessible resources becomes visible.
The operational impact of this vulnerability allows an authenticated attacker to enumerate sensitive information about restricted knowledge bases without needing elevated privileges or exploiting a secondary authentication bypass. By analyzing the returned identifiers, names, and descriptions from the shared collection, an adversary can map out the organization's internal data structures and infer the existence of confidential projects, proprietary research, or private operational details that were intended to be hidden. This information disclosure facilitates further reconnaissance efforts, potentially aiding in targeted social engineering attacks or providing context for more sophisticated exploitation attempts against other components of the AI platform infrastructure. The ability to discover these metadata elements undermines the principle of least privilege and compromises the confidentiality guarantees expected from a secure multi-tenant environment.
To mitigate this risk, organizations running affected versions must immediately upgrade Open WebUI to version 0.11.1 or later, where the integration between the application layer filters and vector backend search methods has been corrected to ensure strict adherence to access control policies. In environments where immediate patching is not feasible due to operational constraints, administrators should consider implementing network-level restrictions to limit exposure of the Open WebUI interface to untrusted networks and enforce rigorous identity verification processes at a reverse proxy or API gateway layer before requests reach the application backend. Additionally, auditing logs for unusual enumeration patterns involving knowledge base metadata can help detect ongoing exploitation attempts while remediation efforts are underway.