CVE-2025-7394
Published: 18 July 2025
Summary
CVE-2025-7394 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Wolfssl Wolfssl. Its CVSS base score is 7.0 (High).
Operationally, ranked in the top 45.6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21938
Vulnerability details
In the OpenSSL compatibility layer implementation, the function RAND_poll() was not behaving as expected and leading to the potential for predictable values returned from RAND_bytes() after fork() is called. This can lead to weak or predictable random numbers generated in…
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applications that are both using RAND_bytes() and doing fork() operations. This only affects applications explicitly calling RAND_bytes() after fork() and does not affect any internal TLS operations. Although RAND_bytes() documentation in OpenSSL calls out not being safe for use with fork() without first calling RAND_poll(), an additional code change was also made in wolfSSL to make RAND_bytes() behave similar to OpenSSL after a fork() call without calling RAND_poll(). Now the Hash-DRBG used gets reseeded after detecting running in a new process. If making use of RAND_bytes() and calling fork() we recommend updating to the latest version of wolfSSL. Thanks to Per Allansson from Appgate for the report.
- 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.