Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-0428 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 LLM Application 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 versions through 1.8.96. It stems from unsafe deserialization of untrusted input supplied via the $form['post_content'] variable inside the wpaicg_export_prompts function, which is tracked under CWE-502. The plugin itself contains no POP chain, so the issue is only exploitable to its full extent when another plugin or theme on the same site supplies one.
An attacker with administrative privileges can supply a crafted serialized object through the affected export function. Successful exploitation with a usable POP chain present elsewhere on the target could permit deletion of arbitrary files, disclosure of sensitive data, or arbitrary code execution; the CVSS 7.2 score reflects the high impact under these conditions.
The referenced WordPress plugin changeset documents the fix that was applied to the wpaicg_export_prompts code path, while the Wordfence advisory supplies additional technical detail for detection and remediation.
EPSS for the CVE rose from a low baseline to a peak of 0.0147 on 2025-12-11 before receding, indicating that exploitation interest increased several months after public disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-1662
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_prompts 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
- LLM Application 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.