CVE-2025-14929

A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths. As a result, attacker-controlled Python code from custom_generate/generate.py is copied into the user's ~/.cache/huggingface/modules directory even if the user declines the trust prompt.

Hugging Face Transformers fails to validate shard filenames in checkpoint index files, allowing attackers to read arbitrary files outside the model directory. Malicious index files with parent-directory references or absolute paths can lead to file disclosure and filesystem reconnaissance.

Hugging Face Transformers up to version 5.8.0.dev0 has an arbitrary file write vulnerability via path traversal in save_pretrained(). Keys from chat_template are used as filenames without validation.

Deserialization of untrusted data in Hugging Face Transformers for the GLM4 model when parsing weights. Lack of validation of user-supplied data can lead to deserialization of untrusted data and remote code execution.

Code injection in the convert_config function of Hugging Face Transformers for the HuBERT model. Lack of validation of a user-supplied string before using it to execute Python code allows remote code execution.

Code injection in the convert_config function of Hugging Face Transformers for the SEW-D model. Lack of validation of a user-supplied string before using it to execute Python code allows remote code execution.

Code injection in the convert_config function of Hugging Face Transformers for the SEW model. Lack of validation of a user-supplied string before using it to execute Python code allows remote code execution.

Deserialization of untrusted data in Hugging Face Transformers for the megatron_gpt2 model when parsing checkpoints. Lack of validation of user-supplied data can lead to deserialization of untrusted data and remote code execution.

Deserialization of untrusted data in Hugging Face Transformers for the Transformer-XL model when parsing model files. Lack of validation of user-supplied data can lead to deserialization of untrusted data and remote code execution.

A vulnerability in Hugging Face Transformers' Perceiver model parsing involves deserialization of untrusted data without proper validation. Exploitation requires user interaction (opening a malicious file or visiting a malicious page). It can lead to remote code execution in the context of the current user.