CVE-2021-22925
Published: 05 August 2021
Summary
CVE-2021-22925 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Apple Macos. Its CVSS base score is 5.3 (Medium).
Operationally, ranked at the 46.3th 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-2021-10054
Vulnerability details
curl supports the `-t` command line option, known as `CURLOPT_TELNETOPTIONS`in libcurl. This rarely used option is used to send variable=content pairs toTELNET servers.Due to flaw in the option parser for sending `NEW_ENV` variables, libcurlcould be made to pass on uninitialized…
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data from a stack based buffer to theserver. Therefore potentially revealing sensitive internal information to theserver using a clear-text network protocol.This could happen because curl did not call and use sscanf() correctly whenparsing the string provided by the application.
- 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.