CVE-2024-11394
Published: 22 November 2024
Summary
CVE-2024-11394 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 1.5% 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 Supply Chain Compromise (AML.T0010).
Deeper analysis
CVE-2024-11394 is a remote code execution vulnerability in Hugging Face Transformers stemming from unsafe deserialization of untrusted data when processing Trax model files. The flaw, tracked as ZDI-CAN-25012 and assigned CWE-502, arises from insufficient validation of user-supplied model data, enabling arbitrary code execution on affected installations. It carries a CVSS 3.1 score of 8.8.
Remote attackers can exploit the issue by supplying a malicious model file or hosting a malicious page that the target must open or visit, resulting in code execution under the privileges of the current user. No authentication is required beyond this user interaction.
The Zero Day Initiative advisory ZDI-24-1515 addresses the vulnerability and is the primary public reference for affected versions and remediation guidance.
The affected component is part of the Hugging Face Transformers library used in machine-learning workflows. The EPSS score has reached 0.6505 without a documented rise from a lower baseline.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-34103
Vulnerability details
Hugging Face Transformers Trax Model 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…
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target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of model 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-25012.
- 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
- Hugging Face Transformers is a library for NLP and Transformer models, and the vulnerability specifically affects deserialization of Trax model files within it.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization of untrusted Trax model data in Hugging Face Transformers enables remote code execution upon user interaction (visiting malicious page or opening malicious file), directly mapping to 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.