CVE-2022-24719
Published: 01 March 2022
Summary
CVE-2022-24719 is a low-severity Exposure of Private Personal Information to an Unauthorized Actor (CWE-359) vulnerability in Fluture-Node Project Fluture-Node. Its CVSS base score is 2.6 (Low).
Operationally, ranked in the top 41.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-0215
Vulnerability details
Fluture-Node is a FP-style HTTP and streaming utils for Node based on Fluture. Using `followRedirects` or `followRedirectsWith` with any of the redirection strategies built into fluture-node 4.0.0 or 4.0.1, paired with a request that includes confidential headers such as Authorization…
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or Cookie, exposes you to a vulnerability where, if the destination server were to redirect the request to a server on a third-party domain, or the same domain over unencrypted HTTP, the headers would be included in the follow-up request and be exposed to the third party, or potential http traffic sniffing. The redirection strategies made available in version 4.0.2 automatically redact confidential headers when a redirect is followed across to another origin. A workaround has been identified by using a custom redirection strategy via the `followRedirectsWith` function. The custom strategy can be based on the new strategies available in fluture-node@4.0.2.
- 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.
Explicit procedures to delete inaccurate or outdated PII directly mitigate improper removal of sensitive information before storage or transfer.
The explicit requirement to delete inaccurate/outdated PII implements proper removal of sensitive information before further storage or transfer.
The control implements proper removal of sensitive information before storage or transfer of datasets.
Automated marking identifies private personal information in outputs, tangibly reducing the ability to exploit weaknesses that result in its unauthorized exposure.
Privacy-specific attributes and their controlled association directly reduce exposure of private personal information through missing or incorrect labeling.
Preventing nonpublic personal information from public posting reduces unauthorized exposure of private personal data.
The control detects and protects against mining of private personal information, reducing unauthorized exposure of PII.
Privacy literacy training directly targets preventing exposure of personal information through user mishandling.