CVE-2023-27478
Published: 07 March 2023
Summary
CVE-2023-27478 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Awesome Libmemcached. Its CVSS base score is 6.5 (Medium).
Operationally, ranked in the top 41.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-31250
Vulnerability details
libmemcached-awesome is an open source C/C++ client library and tools for the memcached server. `libmemcached` could return data for a previously requested key, if that previous request timed out due to a low `POLL_TIMEOUT`. This issue has been addressed in…
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version 1.1.4. Users are advised to upgrade. There are several ways to workaround or lower the probability of this bug affecting a given deployment. 1: use a reasonably high `POLL_TIMEOUT` setting, like the default. 2: use separate libmemcached connections for unrelated data. 3: do not re-use libmemcached connections in an unknown state.
- 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.