CVE-2023-28322
Published: 26 May 2023
Summary
CVE-2023-28322 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Apple Macos. Its CVSS base score is 3.7 (Low).
Operationally, ranked in the top 29.2% 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-2023-32029
Vulnerability details
An information disclosure vulnerability exists in curl <v8.1.0 when doing HTTP(S) transfers, libcurl might erroneously use the read callback (`CURLOPT_READFUNCTION`) to ask for data to send, even when the `CURLOPT_POSTFIELDS` option has been set, if the same handle previously wasused…
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to issue a `PUT` request which used that callback. This flaw may surprise the application and cause it to misbehave and either send off the wrong data or use memory after free or similar in the second transfer. The problem exists in the logic for a reused handle when it is (expected to be) changed from a PUT to a POST.
- 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.