CVE-2025-14350
vulnerability analysis and mitigation

Overview

CVE-2025-14350 is an information disclosure vulnerability in Mattermost Server that allows authenticated users to enumerate teams and their URL names by exploiting improper team membership validation during channel mention processing. Affected versions include 10.11.x ≤ 10.11.9, 11.1.x ≤ 11.1.2, and 11.2.x ≤ 11.2.1. The vulnerability was published on February 16, 2026, and is tracked under Mattermost Advisory ID MMSA-2025-00563. It carries a CVSS v3.1 base score of 4.3 (Medium) (Red Hat Advisory, Mattermost Security Updates).

Technical details

The root cause is a missing authorization check (CWE-862) when Mattermost processes channel mentions in posts. When an authenticated user posts a channel shortlink, the server fails to validate whether the posting user is a member of the team associated with the mentioned channel. As a result, the channel_mentions property in the API response leaks team existence and URL name information to unauthorized users. No special privileges beyond a valid authenticated session are required, and the attack is conducted entirely over the network with low complexity (Red Hat Advisory, Mattermost Security Updates).

Impact

Successful exploitation results in limited confidentiality impact — specifically, the disclosure of team existence and URL names within a Mattermost instance. This exposes organizational structure information (e.g., team names and identifiers) that should be restricted to authorized members, potentially aiding further reconnaissance or social engineering. There is no impact on integrity or availability, and the vulnerability does not enable direct lateral movement or code execution (Red Hat Advisory).

Exploitation steps

  1. Authenticate: Log in to a vulnerable Mattermost instance (versions 10.11.0–10.11.9, 11.1.0–11.1.2, or 11.2.0–11.2.1) with any valid user account.
  2. Post a channel shortlink: In any accessible channel, compose a post containing a channel shortlink (e.g., ~channel-name) that references a channel in a team the attacker is not a member of.
  3. Observe the API response: Capture the API response to the post creation request and inspect the channel_mentions property in the JSON payload.
  4. Enumerate teams: Analyze the channel_mentions data to extract team existence and URL name information for teams the attacker should not have access to, building a map of the organization's team structure (Red Hat Advisory, Mattermost Security Updates).

Indicators of compromise

  • Logs: Mattermost server access logs showing repeated post creation requests from a single authenticated user containing channel shortlinks (~channel-name) referencing channels across multiple teams the user is not a member of.
  • Network: Unusual volume of API calls to post-creation endpoints (e.g., /api/v4/posts) with channel mention payloads, particularly targeting channels in teams outside the user's membership.
  • Behavioral: A single user account generating a high frequency of posts with channel shortlinks in a short time window, consistent with automated enumeration of team/channel combinations.

Mitigation and workarounds

Mattermost has released patched versions that address this vulnerability: 10.11.10, 11.1.3, and 11.2.2 or later. Organizations should upgrade to one of these fixed versions as the primary remediation. As interim measures, administrators can review access logs for suspicious channel mention activity and implement network monitoring to detect enumeration patterns. No configuration-based workaround is documented; upgrading is the recommended action (Mattermost Security Updates, Red Hat Advisory).

Community reactions

Coverage of CVE-2025-14350 has been limited to vulnerability database aggregators and security advisory feeds, with no notable researcher commentary or significant social media discussion identified. A brief technical write-up was published at infinitsec.net shortly after disclosure, describing the information disclosure mechanism via channel mentions (infinitsec.net).

Additional resources


SourceThis report was generated using AI

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