CVE-2026-74887 jahlives CVE debrief

Who should care

Developers and maintainers of systems and applications using the openssl_encrypt module, especially those relying on cryptographic operations, should be aware of this vulnerability and take steps to mitigate potential risks.

Technical summary

The openssl_encrypt module before version 1.4.0 imports Python's non-cryptographic 'random' module, creating a hazard for future code to inadvertently use insecure random number generation. Although no direct calls to random.* were present in the code, the import could lead to predictable values being used in cryptographic operations if not addressed. The issue was fixed by removing the import in version 1.4.0.

Defensive priority

This vulnerability has a critical CVSS score of 9.3 and could potentially lead to predictable values being used in cryptographic operations if future code inadvertently uses the insecure 'random' module. Immediate attention is required to assess and mitigate potential risks.

Recommended defensive actions

• Review and update openssl_encrypt to version 1.4.0 or later to remove the insecure import.
• Assess the potential impact of this vulnerability on your systems and applications.
• Monitor for any future changes to the openssl_encrypt module that could reintroduce this vulnerability.
• Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up.
• Review the supplied official advisory or CVE record to validate affected scope, severity, and vendor guidance.
• Plan vendor-supported updates or mitigations through normal change control where exposure is confirmed.
• Check relevant monitoring, detection, and logs for exposed assets that need extra review.

Evidence notes

The openssl_encrypt module before version 1.4.0 imports Python's non-cryptographic 'random' module, creating a hazard for future code to inadvertently use insecure random number generation. The import was removed in version 1.4.0. Limited information is available about the vulnerability's impact and affected systems.

Sources and references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.