CVE-2022-21677
Published: 14 January 2022
Summary
CVE-2022-21677 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Discourse Discourse. Its CVSS base score is 4.3 (Medium).
Operationally, ranked in the top 41.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-26889
Vulnerability details
Discourse is an open source discussion platform. Discourse groups can be configured with varying visibility levels for the group as well as the group members. By default, a newly created group has its visibility set to public and the group's…
more
members visibility set to public as well. However, a group's visibility and the group's members visibility can be configured such that it is restricted to logged on users, members of the group or staff users. A vulnerability has been discovered in versions prior to 2.7.13 and 2.8.0.beta11 where the group advanced search option does not respect the group's visibility and members visibility level. As such, a group with restricted visibility or members visibility can be revealed through search with the right search option. This issue is patched in `stable` version 2.7.13, `beta` version 2.8.0.beta11, and `tests-passed` version 2.8.0.beta11 versions of Discourse. There are no workarounds aside from upgrading.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
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.