CVE-2026-6347
Published: 18 May 2026
Summary
CVE-2026-6347 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Mattermost Mattermost Server. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Credentials In Files (T1552.001); ranked at the 16.8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-30752
Vulnerability details
Mattermost versions 11.5.x <= 11.5.1, 10.11.x <= 10.11.13, 11.4.x <= 11.4.3 fail to sanitize sensitive configuration fields in the Mattermost Calls plugin which allows an attacker with access to a support packet to obtain TURN server credentials via the plaintext…
more
values present in the exported plugin configuration.. Mattermost Advisory ID: MMSA-2026-00605
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vulnerability exposes plaintext TURN credentials in exported support packet config, directly enabling credential access from files.
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.