CVE-2024-38647
Published: 22 November 2024
Summary
CVE-2024-38647 is a high-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Qnap Ai Core. Its CVSS base score is 7.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 46.3% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Computer Vision; in the Privacy and Disclosure risk domain; MITRE ATLAS techniques in scope: Obtain Capabilities (AML.T0016), Exfiltration via AI Inference API (AML.T0024).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-37699
Vulnerability details
An exposure of sensitive information vulnerability has been reported to affect QNAP AI Core. If exploited, the vulnerability could allow remote attackers to compromise the security of the system. We have already fixed the vulnerability in the following version: QNAP…
more
AI Core 3.4.1 and later
- CWE(s)
AI Security AnalysisAI
- AI Category
- Computer Vision
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- QNAP AI Core is a proprietary AI platform for deploying and managing AI applications on QNAP NAS devices, which fits 'Other Platforms' as it does not align specifically with frameworks, libraries, NLP/CV, or other listed subcategories.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Remote exploitation of the information disclosure vulnerability in the public-facing QNAP AI Core service enables initial access and system compromise.
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.
The control's identification, isolation, alerting, and eradication steps directly limit the impact and exploitation window of unauthorized sensitive information exposure.
Directly prevents exposure of critical organizational information by applying OPSEC processes across the SDLC.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.