CVE-2022-3861
Published: 21 November 2022
Summary
CVE-2022-3861 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Muffingroup Betheme. Its CVSS base score is 8.8 (High).
Operationally, ranked in the top 11.0% 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-2022-43201
Vulnerability details
The Betheme theme for WordPress is vulnerable to PHP Object Injection in versions up to, and including, 26.5.1.4 via deserialization of untrusted input supplied via the import, mfn-items-import-page, and mfn-items-import parameters passed through the mfn_builder_import, mfn_builder_import_page, importdata, importsinglepage, and importfromclipboard…
more
functions. This makes it possible for authenticated attackers, with subscriber level permissions and above to inject a PHP Object. The additional presence of a POP chain would make it possible for attackers to execute code, retrieve sensitive data, delete files, etc..
- 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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
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.
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.