CVE-2022-2827
Published: 05 December 2022
Summary
CVE-2022-2827 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Ami Megarac Sp-X. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 3.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis
AMI MegaRAC contains a user enumeration vulnerability tracked as CVE-2022-2827. The flaw resides in the remote management firmware used for server baseboard management controllers and is rated 7.5 under CVSS 3.1, reflecting network-accessible exposure that leaks sensitive user information without authentication.
An unauthenticated attacker can send crafted requests over the network to enumerate valid user accounts. Successful exploitation discloses account names, enabling targeted follow-on attacks while leaving integrity and availability unaffected.
The vendor advisory AMI-SA-2023001, referenced in the published references, details the affected MegaRAC versions and corresponding remediation steps. The EPSS score has remained at 0.2781 since disclosure, indicating sustained but not sharply increasing exploitation interest.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-35063
Vulnerability details
AMI MegaRAC User Enumeration 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.