CVE-2026-44827 in diffusers
Summary
by MITRE • 05/14/2026
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 actually loads the module resolves the None value into a valid filename. An attacker can achieve silent arbitrary code execution by publishing a malicious model repository with a None.py file and a standard-looking model_index.json that references a legitimate pipeline class name, requiring only that a victim calls from_pretrained on the repository. This vulnerability is fixed in 0.38.0.
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Analysis
by VulDB Data Team • 05/15/2026
The diffusers library serves as a critical component for implementing and deploying pretrained diffusion models within machine learning applications, providing standardized interfaces for loading various pipeline configurations from sources like the Hugging Face Hub. This vulnerability exists in versions prior to 0.38.0 where the library fails to properly validate user inputs during the pipeline loading process, creating a remote code execution vector that can be exploited without user consent or explicit trust settings. The flaw specifically resides in the _resolve_custom_pipeline_and_cls function within pipeline_loading_utils.py, which employs unsafe string interpolation techniques that allow attackers to manipulate the loading behavior through crafted repository structures.
The technical mechanism behind this vulnerability stems from the improper handling of default parameter values in the pipeline loading workflow. When users do not explicitly provide a custom_pipeline parameter to the DiffusionPipeline.from_pretrained() method, the system defaults to None, which during f-string interpolation becomes the literal string "None.py". This creates a dangerous condition where an attacker can craft a malicious repository on the Hugging Face Hub containing a file named None.py that executes arbitrary code when loaded. The system's trust_remote_code safeguard fails because it only evaluates whether the custom_pipeline parameter was explicitly provided rather than checking if the resolved filename is malicious, allowing the execution to proceed undetected.
The operational impact of this vulnerability is particularly severe as it requires minimal attacker effort to achieve complete system compromise. An attacker only needs to create a repository containing a malicious None.py file alongside a legitimate-looking model_index.json that references a valid pipeline class name. Victims can unknowingly trigger this exploit by calling from_pretrained() on the repository, making the attack invisible to users who might otherwise be cautious about remote code execution risks. This silent execution capability enables attackers to perform arbitrary code execution on systems running vulnerable versions of diffusers, potentially leading to data exfiltration, system compromise, or further lateral movement within network environments.
This vulnerability maps directly to CWE-94, which describes the weakness of executing arbitrary code, and aligns with ATT&CK technique T1059.001 for executing commands through various interfaces. The flaw represents a classic case of unsafe string interpolation combined with improper input validation, where the default parameter handling creates an unexpected code path that bypasses security checks. Organizations using diffusers for machine learning deployments should immediately upgrade to version 0.38.0 or later to mitigate this risk, as the fix addresses the core issue by properly validating and sanitizing the custom_pipeline parameter before proceeding with module loading operations. The vulnerability demonstrates the critical importance of proper parameter validation and input sanitization in security-sensitive code paths, particularly when dealing with external resource loading mechanisms in machine learning frameworks.