CVE-2024-13786
Published: 02 July 2025
Summary
CVE-2024-13786 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Themeforest (inferred from references). Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 14.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-54721
Vulnerability details
The education theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 3.6.10 via deserialization of untrusted input in the 'themerex_callback_view_more_posts' function. This makes it possible for unauthenticated attackers to inject a PHP Object.…
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No known POP chain is present in the vulnerable software, which means this vulnerability has no impact unless another plugin or theme containing a POP chain is installed on the site. If a POP chain is present via an additional plugin or theme installed on the target system, it may allow the attacker to perform actions like delete arbitrary files, retrieve sensitive data, or execute code depending on the POP chain present.
- 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.