HighVulnerability

CVE-2024-11393: Hugging Face Transformers MaskFormer deserialization flaw enables code execution

Source: NVD(opens in a new tab)
Identifier: CVE-2024-11393
Published: November 22, 2024
Record updated: October 9, 2026

Affected: transformers >= 0, < 4.48.0
Fixed in: 4.48.0
released January 10, 2025, 48 days after disclosure
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 3.3%

Summary

CVE-2024-11393 is a remote code execution flaw in the MaskFormer model of Hugging Face Transformers. The flaw sits in the parsing of model files, where user-supplied data is not properly validated, leading to deserialization of untrusted data. A remote attacker can run arbitrary code in the context of the current user, but only if the target visits a malicious page or opens a malicious file.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 8.8(high)
Patch available: Yes
EPSS: 3.3%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-502
Attack pattern (CAPEC): CAPEC-586
AI threat technique (MITRE ATLAS): AML.T0010

Affected

Packages: transformers@>= 0, < 4.48.0 (fixed: 4.48.0)
Vendors: HuggingFace

Classification

Attack type: Supply chain
Attack sophistication: Moderate
AI component targeted: Model
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2024-11393

First tracked: February 15, 2026 at 08:46 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 95% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

transformersPyPILLM dependency since 2016-08-17 · 141 tracked dependents

Topics

Model and package supply chain

Summary

Mitigation

Affected packages in the Exposure Registry

Topics

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