CVE-2023-48225
Published: 12 December 2023
Summary
CVE-2023-48225 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Laf Laf. Its CVSS base score is 8.9 (High).
Operationally, ranked at the 35.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-52298
Vulnerability details
Laf is a cloud development platform. Prior to version 1.0.0-beta.13, the control of LAF app enV is not strict enough, and in certain scenarios of privatization environment, it may lead to sensitive information leakage in secret and configmap. In ES6…
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syntax, if an obj directly references another obj, the name of the obj itself will be used as the key, and the entire object structure will be integrated intact. When constructing the deployment instance of the app, env was found from the database and directly inserted into the template, resulting in controllability here. Sensitive information in the secret and configmap can be read through the k8s envFrom field. In a privatization environment, when `namespaceConf. fixed` is marked, it may lead to the leakage of sensitive information in the system. As of time of publication, it is unclear whether any patches or workarounds exist.
- 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.