CVE-2026-5127 (GCVE-0-2026-5127)
Vulnerability from cvelistv5 – Published: 2026-05-08 08:26 – Updated: 2026-05-08 20:00
VLAI
EPSS
VEX
Title
User Frontend: AI Powered Frontend Posting, User Directory, Profile, Membership & User Registration <= 4.3.1 - Authenticated (Subscriber+) PHP Object Injection
Summary
The User Frontend: AI Powered Frontend Posting, User Directory, Profile, Membership & User Registration plugin for WordPress is vulnerable to Deserialization of Untrusted Data in versions up to, and including, 4.3.1 This is due to insufficient input validation and type checking on the wpuf_files parameter during form submission, combined with unconditional deserialization via maybe_unserialize() when displaying post content. This makes it possible for authenticated attackers, with Subscriber-level access and above, to inject arbitrary PHP objects, which can be leveraged to execute arbitrary code, delete arbitrary files, or perform other malicious actions if a POP chain is present on the target system.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-08 19:59 UTC
CWE
- CWE-502 - Deserialization of Untrusted Data
Assigner
References
19 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| wedevs | User Frontend: AI Powered Frontend Posting, User Directory, Profile, Membership & User Registration |
Affected:
0 , ≤ 4.3.1
(semver)
|
guessed |
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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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