CVE-2024-11392
Published: 22 November 2024
Summary
CVE-2024-11392 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Huggingface Transformers. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked in the top 1.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment risk domain; MITRE ATLAS techniques in scope: AI Software (AML.T0010.001), Invert AI Model (AML.T0024.001), Reputational Harm (AML.T0048.001).
Deeper analysis
Hugging Face Transformers contains a deserialization of untrusted data vulnerability in its MobileViTV2 component. The flaw stems from insufficient validation of user-supplied data in configuration file handling, which permits remote code execution when malicious input is processed. The issue was originally reported as ZDI-CAN-24322 and carries a CVSS 3.1 score of 8.8.
Remote attackers can exploit the vulnerability without authentication by supplying a crafted configuration file or page that the target must open or visit. Successful exploitation results in arbitrary code execution in the context of the current user, affecting confidentiality, integrity, and availability.
The Zero Day Initiative advisory ZDI-24-1513 provides further details on the issue. The associated EPSS score of 0.5929 indicates substantial exploitation interest since disclosure for this machine-learning library component.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-33791
Vulnerability details
Hugging Face Transformers MobileViTV2 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target…
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must visit a malicious page or open a malicious file. The specific flaw exists within the handling of configuration files. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current user. Was ZDI-CAN-24322.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- The vulnerability affects Hugging Face Transformers library, specifically the MobileViTV2 model handling, which is part of the Transformers ecosystem focused on NLP and Transformer architectures, including vision transformers.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization of untrusted data in Hugging Face Transformers configuration files enables remote code execution with required user interaction (visiting malicious page or opening malicious file), facilitating Exploitation for Client Execution.
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.