CVE-2024-11393
RCE in Huggingface Transformers ≤ 4.48.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-11393 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 Junk Data (T1001.001); ranked in the top 13% 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.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-11393 is a deserialization of untrusted data vulnerability, tracked as CWE-502, that affects Hugging Face Transformers when parsing MaskFormer model files. The flaw stems from insufficient validation of user-supplied data during model file processing and carries a CVSS 3.1 score of 8.8.
Remote attackers can exploit the issue without authentication by supplying a malicious model file or page; successful exploitation results in arbitrary code execution in the context of the current user, though user interaction is required to trigger the payload.
The single referenced advisory from the Zero Day Initiative (ZDI-24-1514) confirms the vulnerability was originally reported as ZDI-CAN-25191 but does not detail specific patches or workarounds in the supplied information.
The vulnerability impacts an AI/ML framework component, and its EPSS score has remained steady at a peak of 0.7953 since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-34102
Vulnerability Data
Hugging Face Transformers MaskFormer 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 parsing 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-25191.
- 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 in handling the MaskFormer model during deserialization of model files. Hugging Face Transformers is a core library for transformer-based models, primarily associated with NLP and transformer architectures, even though MaskFormer is used for computer vision tasks.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.