PYSEC-2026-2446

Background

This vulnerability is found in the diffusers package - the transformers-equivalent library for diffusion models.

It is found in the DiffusionPipeline.from_pretrained flow, which is used to load a pipeline from the HuggingFace Hub.

This function has a trust_remote_code guard: if the repository’s model_index.json references a custom pipeline class defined in a .py file in the repo, the load is blocked unless trust_remote_code=True is explicitly passed:

ValueError: The repository for attacker/repo contains custom code in pipeline.py
which must be executed to correctly load the model. You can inspect the repository
content at https://hf.co/attacker/repo/blob/main/pipeline.py.
Please pass the argument `trust_remote_code=True` to allow custom code to be run.

The vulnerability allows arbitrary code execution through the custom pipeline flow from a Hub repo, with no custom_pipeline or trust_remote_code kwargs passed. The from_pretrained call succeeds and returns a functional pipeline.

Naive Flow

DiffusionPipeline.from_pretrained begins by popping all relevant arguments from kwargs into local variables, then calls DiffusionPipeline.download() to fetch the repo files:

# pipeline_utils.py:853
cached_folder = cls.download(
    pretrained_model_name_or_path,
    ...
    custom_pipeline=custom_pipeline,
    trust_remote_code=trust_remote_code,
    ...
)

Inside download(), model_index.json is fetched first as a standalone file via hf_hub_download:

# pipeline_utils.py:1636
config_file = hf_hub_download(
    pretrained_model_name,
    cls.config_name,
    ...
)
config_dict = cls._dict_from_json_file(config_file)

This config is used to detect custom pipeline code and enforce the trust check:

# pipeline_utils.py:1672
if custom_pipeline is None and isinstance(config_dict["_class_name"], (list, tuple)):
    custom_pipeline = config_dict["_class_name"][0]

load_pipe_from_hub =...