CVE-2026-19603 in Mattermost
Summary
by MITRE • 10/16/2026
Mattermost versions 11.9.x <= 11.9.1, 11.8.x <= 11.8.5, 11.7.x <= 11.7.10, 11.10.x <= 11.10.1 fail to sanitize the active channel field returned by the user status API endpoints, which allows an authenticated user to learn the channel ID of a private channel another user is actively viewing via the status endpoints, without being a member of that channel. Mattermost Advisory ID: MMSA-2026-00765
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Analysis
by VulDB Data Team • 10/16/2026
The vulnerability identified in Mattermost versions 11.9.x through 11.10.1 represents a significant information disclosure flaw within the platform's user status API endpoints. Specifically, the application fails to properly sanitize or filter the active channel identifier when returning user presence data. This technical deficiency allows an authenticated attacker who is not a member of a specific private channel to infer which private channels other users are actively viewing by inspecting the channel ID included in the response payload from the status endpoint. The core issue lies in the server-side logic that aggregates and exposes real-time activity metrics without enforcing strict access control checks against the sensitive resource being accessed, namely the private channel membership list.
From a technical perspective, this flaw constitutes an improper privilege escalation of information visibility rather than direct code execution or system compromise. When a user interacts with Mattermost, their current active context is tracked for features such as presence indicators and activity feeds. However, in these affected versions, the backend service returns the specific channel identifier associated with that active state to any authenticated requester querying the status API, regardless of whether the requesting user has permission to view or participate in that private channel. This behavior violates the principle of least privilege by exposing internal structural identifiers that should remain opaque to unauthorized parties. The vulnerability is classified under CWE-200 as an Information Exposure where security-sensitive information is disclosed without explicit authorization, and it aligns with ATT&CK technique T1537 which involves communicating through a different medium or protocol than intended to evade detection, although in this case the communication channel itself is legitimate but misused due to insufficient filtering.
The operational impact of this vulnerability extends beyond mere curiosity-driven reconnaissance. By mapping out private channels that are actively being used by other users, an attacker can gain valuable intelligence about organizational structure and ongoing projects without gaining direct access to the content within those channels. This knowledge can facilitate more targeted social engineering attacks or help identify high-value targets for further exploitation attempts. Furthermore, it undermines the confidentiality guarantees expected in secure collaboration platforms where private channels are intended to isolate sensitive discussions from unauthorized viewers. The ability to enumerate active participants and their contexts effectively reduces the security boundary of private communications, potentially leading to privacy violations and increased risk profile for organizations relying on Mattermost for confidential operations.
Mitigation strategies must prioritize immediate patching as well as defensive configuration adjustments. Organizations running affected versions should upgrade immediately to a patched release that addresses MMSA-2026-00765 by ensuring the server enforces strict access control checks before returning channel identifiers in status responses. Until patches are applied, administrators can consider restricting API endpoint access via network-level controls or Web Application Firewalls if possible, although this is less effective than a code fix since authentication tokens would still be required to reach the endpoints. Additionally, implementing enhanced monitoring for unusual patterns of API calls targeting user status data may help detect exploitation attempts in real-time. Long-term remediation should involve integrating automated security testing into the CI/CD pipeline to catch similar input validation and authorization bypasses before deployment, ensuring that all API responses are rigorously filtered against the requester's permission set relative to every piece of sensitive metadata returned.