Attack Chain

1. An attacker creates a Hugging Face Hub repository with a model_index.json file containing a plain string _class_name value, indicating no custom pipeline code.
2. A user attempts to load the pipeline using DiffusionPipeline.from_pretrained("attacker/repo").
3. The hf_hub_download function fetches the model_index.json file (commit A) and the trust check passes because no custom pipeline is detected.
4. Before the snapshot_download function is called, the attacker pushes a new commit (commit B) to the repository, modifying the model_index.json file to use a list _class_name and adding a malicious pipeline.py file.
5. The snapshot_download function fetches commit B, including the malicious pipeline.py file.
6. The _resolve_custom_pipeline_and_cls function reads the updated model_index.json and resolves the custom pipeline to the local path of the pipeline.py file.
7. The _get_pipeline_class function imports the malicious pipeline.py file without any further trust checks.
8. The malicious code within pipeline.py is executed, resulting in arbitrary code execution on the user's machine.

Impact

Successful exploitation of this vulnerability allows an attacker to execute arbitrary code on the victim's machine. The vulnerability is a silent RCE meaning that the from_pretrained call succeeds and returns a fully functional pipeline even when malicious code has been injected. This could lead to data exfiltration, system compromise, or other malicious activities. The impact is significant as it undermines the trust mechanisms designed to protect users from running untrusted code.

Recommendation

• Upgrade to diffusers version 0.38.0 or later to patch the vulnerability.
• When using DiffusionPipeline.from_pretrained, pin the revision argument to a specific commit hash to avoid race conditions, as described in the overview.
• Deploy the Sigma rule "Detect Diffusers from_pretrained with trust_remote_code" to detect potential exploitation attempts by identifying calls to DiffusionPipeline.from_pretrained without explicit trust settings.
• Monitor network connections for unexpected outbound traffic originating from processes associated with the diffusers library, using the network connection Sigma rule in this brief to identify potential command and control activity.