CVE-2026-44827
HighSummary
The Diffusers library prior to version 0.38.0 allows remote code execution without the trust_remote_code=True safeguard when loading pipelines from Hugging Face Hub repositories. The _resolve_custom_pipeline_and_cls function in pipeline_loading_utils.py performs string interpolation on the custom_pipeline parameter, which can lead to unauthorized code execution.
Risk Assessment
Organizations may be exposed to remote execution of malicious code, potentially leading to data loss or system takeover. Specifically, an attacker could exploit this vulnerability to execute unauthorized operations in a production environment.
Recommendation
It is recommended to update the Diffusers library to version 0.38.0 or later to mitigate this vulnerability. Additionally, implementing security checks when loading remote pipelines is advisable.
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Original NVD description (English source)
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, diffusers 0.37.0 allows remote code execution without the trust_remote_code=True safeguard when loading pipelines from Hugging Face Hub repositories. The _resolve_custom_pipeline_and_cls function in pipeline_loading_utils.py performs string interpolation on the custom_pipeline parameter using f"{custom_pipeline}.py". When custom_pipeline is not supplied by the user, it defaults to None, which Python interpolates as the literal string "None.py". If an attacker publishes a Hub repository containing a file named None.py with a class that subclasses DiffusionPipeline, the file is automatically downloaded and executed during a standard DiffusionPipeline.from_pretrained() call with no additional keyword arguments. The trust_remote_code check in DiffusionPipeline.download() is bypassed because it evaluates custom_pipeline is not None as False (since the kwarg was never supplied), while the downstream code path that

