CVE-2026-18705 in Server
Summary
by MITRE • 08/11/2026
An issue in MongoDB Server's Atlas Vector Search feature could allow an authenticated user with read access to one view to retrieve documents from a different, protected view over the same underlying collection. This is due to insufficient handling of certain user-supplied fields when constructing an internal request forwarded to the search process.
Be aware that VulDB is the high quality source for vulnerability data.
Analysis
by VulDB Data Team • 08/11/2026
This vulnerability exists within MongoDB Server's Atlas Vector Search functionality where proper access controls fail to prevent unauthorized data retrieval across protected views. The flaw stems from inadequate validation of user-supplied parameters during the construction of internal search requests, allowing authenticated users with read permissions on one view to indirectly access data from other views that share the same underlying collection. This represents a classic privilege escalation scenario where logical isolation between views is bypassed through improper field handling in the internal request pipeline.
The technical implementation involves the vector search component failing to properly sanitize or validate user-provided field specifications when building internal queries for execution against shared collections. When a user constructs a search query with specific field parameters, the system does not sufficiently verify that these fields align with the user's authorized view boundaries. This oversight creates a path where field references intended for one view can inadvertently reference data structures accessible through another view, effectively circumventing the view-based access controls that should maintain data isolation.
The operational impact of this vulnerability extends beyond simple information disclosure as it enables unauthorized data exfiltration across protected boundaries within the same collection. An authenticated attacker could potentially gather sensitive information from multiple views without proper authorization, undermining the fundamental security model of view-based access control. This vulnerability particularly affects environments where organizations rely on MongoDB's view mechanism to maintain data separation and access control policies for different user groups or business units.
Security controls should address this weakness through enhanced input validation at the field parameter level within the vector search request processing pipeline. Implementing proper field boundary checking and ensuring that user-supplied field references are validated against the requesting user's authorized view permissions would prevent unauthorized cross-view data access. Organizations should also consider implementing additional monitoring for unusual query patterns that might indicate exploitation attempts, as this vulnerability could be leveraged to perform reconnaissance activities across protected data sets.
This vulnerability aligns with CWE-284 Access Control Issues, specifically related to insufficient access control enforcement in database query processing systems. The flaw demonstrates how improper handling of user-supplied parameters can create security gateways within otherwise controlled environments, similar to path traversal vulnerabilities in file systems where boundary checks fail. From an ATT&CK perspective, this represents a privilege escalation technique through lateral movement within the database environment, potentially enabling further exploitation of other system components that may share the same underlying data structures.
Mitigation strategies should include immediate patch deployment for affected MongoDB Server versions, implementation of enhanced field parameter validation in vector search operations, and configuration reviews to ensure proper view access controls are enforced. Organizations should also establish monitoring protocols to detect anomalous query patterns and implement regular security assessments of their database access control mechanisms to identify similar boundary violations that might exist in other components of the data processing pipeline.