CVE-2022-41917
Published: 16 November 2022
Summary
CVE-2022-41917 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Amazon Opensearch. Its CVSS base score is 4.3 (Medium).
Operationally, ranked at the 48.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-45047
Vulnerability details
OpenSearch is a community-driven, open source fork of Elasticsearch and Kibana. OpenSearch allows users to specify a local file when defining text analyzers to process data for text analysis. An issue in the implementation of this feature allows certain specially…
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crafted queries to return a response containing the first line of text from arbitrary files. The list of potentially impacted files is limited to text files with read permissions allowed in the Java Security Manager policy configuration. OpenSearch version 1.3.7 and 2.4.0 contain a fix for this issue. Users are advised to upgrade. There are no known workarounds for this issue.
- 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.