CVE-2023-45143
Published: 12 October 2023
Summary
CVE-2023-45143 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Fedoraproject Fedora. Its CVSS base score is 3.9 (Low).
Operationally, ranked at the 29.9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-2832
Vulnerability details
Undici is an HTTP/1.1 client written from scratch for Node.js. Prior to version 5.26.2, Undici already cleared Authorization headers on cross-origin redirects, but did not clear `Cookie` headers. By design, `cookie` headers are forbidden request headers, disallowing them to be…
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set in RequestInit.headers in browser environments. Since undici handles headers more liberally than the spec, there was a disconnect from the assumptions the spec made, and undici's implementation of fetch. As such this may lead to accidental leakage of cookie to a third-party site or a malicious attacker who can control the redirection target (ie. an open redirector) to leak the cookie to the third party site. This was patched in version 5.26.2. There are no known workarounds.
- 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.