CVE-2025-46332
Published: 02 May 2025
Summary
CVE-2025-46332 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability. Its CVSS base score is 6.5 (Medium).
Operationally, ranked in the top 34.9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-13288
Vulnerability details
Flags SDK is an open-source feature flags toolkit for Next.js and SvelteKit. Impacted versions include flags from 3.2.0 and prior and @vercel/flags from 3.1.1 and prior as certain circumstances allows a bad actor with detailed knowledge of the vulnerability to…
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list all flags returned by the flags discovery endpoint (.well-known/vercel/flags). This vulnerability allows for information disclosure, where a bad actor could gain access to a list of all feature flags exposed through the flags discovery endpoint, including the flag names, flag descriptions, available options and their labels (e.g. true, false), and default flag values. This issue has been patched in flags@4.0.0, users of flags and @vercel/flags should also migrate to flags@4.0.0.
- 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.