CVE-2024-42222
Published: 07 August 2024
Summary
CVE-2024-42222 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Apache Cloudstack. Its CVSS base score is 4.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Cloud Service Discovery (T1526); ranked in the top 32.6% 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-2024-39461
Vulnerability details
In Apache CloudStack 4.19.1.0, a regression in the network listing API allows unauthorised list access of network details for domain admin and normal user accounts. This vulnerability compromises tenant isolation, potentially leading to unauthorised access to network details, configurations and…
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data. Affected users are advised to upgrade to version 4.19.1.1 to address this issue. Users on older versions of CloudStack considering to upgrade, can skip 4.19.1.0 and upgrade directly to 4.19.1.1.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability in the network listing API allows domain admins and normal users unauthorized access to network details, configurations, and data across tenants, breaking isolation and enabling cloud service discovery.
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.