CVE-2024-22309
Published: 24 January 2024
Summary
CVE-2024-22309 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Quantumcloud Wpbot. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 46.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Other ATLAS/OWASP Terms risk domain; MITRE ATLAS techniques in scope: AI Model Inference API Access (AML.T0040), External Harms (AML.T0048), Exfiltration via AI Inference API (AML.T0024).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-19870
Vulnerability details
Deserialization of Untrusted Data vulnerability in QuantumCloud ChatBot with AI.This issue affects ChatBot with AI: from n/a through 5.1.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- The vulnerability affects the 'ChatBot with AI' WordPress plugin (also referred to as QuantumCloud ChatBot with AI or WordPress AI ChatBot Plugin), which provides AI-powered chatbot functionality for websites, fitting the Enterprise AI Assistants category as it integrates AI assistance into enterprise web platforms.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unauthenticated PHP Object Injection via deserialization of untrusted data in a public-facing WordPress plugin enables exploitation of a public-facing application (T1190), potentially leading to remote code execution, SQL injection, path traversal, or denial of service if POP chain gadgets are available.
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
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.