CVE-2023-7064
Published: 02 May 2024
Summary
CVE-2023-7064 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Averta Shortcodes And Extra Features For Phlox Theme. Its CVSS base score is 7.5 (High).
Operationally, ranked in the top 25.1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-59251
Vulnerability details
The Shortcodes and extra features for Phlox theme plugin for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 2.17.5 via deserialization of untrusted input from the vulnerable 'id' parameter in the 'auxin_template_control_importer' function. This…
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makes it possible for authenticated attackers able to upload a separate PHAR payload as an image file to inject a PHP Object, though the action itself is available to subscribers. No POP chain is present in the vulnerable plugin. If a POP chain is present via an additional plugin or theme installed on the target system, it could allow the attacker to delete arbitrary files, retrieve sensitive data, or execute code.
- 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.