CVE-2022-21663
Published: 06 January 2022
Summary
CVE-2022-21663 is a medium-severity Injection (CWE-74) vulnerability in Debian Debian Linux. Its CVSS base score is 6.6 (Medium).
Operationally, ranked in the top 45.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-26880
Vulnerability details
WordPress is a free and open-source content management system written in PHP and paired with a MariaDB database. On a multisite, users with Super Admin role can bypass explicit/additional hardening under certain conditions through object injection. This has been patched…
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in WordPress version 5.8.3. Older affected versions are also fixed via security release, that go back till 3.7.37. We strongly recommend that you keep auto-updates enabled. There are no known workarounds for this issue.
- 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.
Developer assessments and testing (including injection-focused techniques) identify improper neutralization of special elements, and the verifiable flaw remediation corrects them pre-deployment.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Identifies indicators of injection attacks (command, SQL, LDAP, etc.) via anomaly and attack monitoring.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.