Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-0429 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Aipower Aipower. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 48th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
The vulnerability is a PHP Object Injection flaw in the AI Power: Complete AI Pack plugin for WordPress, affecting all versions through 1.8.96. It stems from unsafe deserialization of untrusted input supplied via the $form['post_content'] variable inside the wpaicg_export_ai_forms() function, which is tracked under CWE-502. The issue carries a CVSS 3.1 score of 7.2.
Authenticated attackers who possess administrative privileges can supply a crafted payload to inject a PHP object. Although the plugin itself contains no POP chain, the presence of an additional vulnerable plugin or theme on the same site could enable the attacker to delete arbitrary files, exfiltrate sensitive data, or achieve remote code execution.
A fix has been published in the WordPress plugin repository, as referenced in the linked changeset and Wordfence advisory.
The EPSS score rose from a low baseline to a peak of 0.0147 on 2025-12-11 before receding to the current value of 0.0036, indicating that exploitation interest increased after public disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-1663
Vulnerability Data
The "AI Power: Complete AI Pack" plugin for WordPress is vulnerable to PHP Object Injection in versions up to, and including, 1.8.96 via deserialization of untrusted input from the $form['post_content'] variable through the wpaicg_export_ai_forms() function. This allows authenticated attackers, with…
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administrative privileges, to inject a PHP Object. 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)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.