MediumVulnerability

CVE-2026-80047: Hugging Face Transformers writes remote Python files to disk before trust check

Source: NVD(opens in a new tab)
Identifier: CVE-2026-80047
Published: September 1, 2026
Record updated: October 9, 2026

Affected: transformers >= 4.49.0, <= 5.8.1
Fixed in: No fixed version was stated when the source was last read.
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: <0.1%

Summary

A vulnerability in Hugging Face Transformers (versions 4.49.0, <= 5.8.1) lets remote Python files be written to local disk without user consent when GenerativePreTrainedModel.load_custom_generate() is used. The function fetches and caches attacker-controlled custom_generate/generate.py into ~/.cache/huggingface/modules before the trust_remote_code consent check, so the file persists even if the user declines the trust prompt. The write is caused by an unconditional file write in dynamic_module_utils.py.

Mitigation

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

Details

Scores and exploitation

EPSS: <0.1%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.
Disclosure date: September 1, 2026

Weaknesses and techniques

AI threat technique (MITRE ATLAS): AML.T0010

Affected

Packages: transformers@>= 4.49.0, <= 5.8.1
Vendors: HuggingFace

Classification

Attack type: Supply chain
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Integrity,
Confidentiality
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-2026-80047

First tracked: September 1, 2026 at 02:08 PM ET, from NVD/CVE Database

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

Classifier confidence: 90% (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

Summary

Mitigation

Affected packages in the Exposure Registry

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