CVE-2022-23634
Published: 11 February 2022
Summary
CVE-2022-23634 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Rubyonrails Rails. Its CVSS base score is 8.0 (High).
Operationally, ranked in the top 34.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-1175
Vulnerability details
Puma is a Ruby/Rack web server built for parallelism. Prior to `puma` version `5.6.2`, `puma` may not always call `close` on the response body. Rails, prior to version `7.0.2.2`, depended on the response body being closed in order for its…
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`CurrentAttributes` implementation to work correctly. The combination of these two behaviors (Puma not closing the body + Rails' Executor implementation) causes information leakage. This problem is fixed in Puma versions 5.6.2 and 4.3.11. This problem is fixed in Rails versions 7.02.2, 6.1.4.6, 6.0.4.6, and 5.2.6.2. Upgrading to a patched Rails _or_ Puma version fixes the vulnerability.
- 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.