Arbitrary Code Injection Affecting diffusers package, versions [,0.38.0)
Threat Intelligence
This vulnerability is trending on Twitter; this may indicate a growing threat.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
Do your applications use this vulnerable package?
In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.
Test your applicationsSnyk Learn
Learn about Arbitrary Code Injection vulnerabilities in an interactive lesson.
Start learning- Snyk IDSNYK-PYTHON-DIFFUSERS-16439019
- published7 May 2026
- disclosed7 May 2026
- creditVancir
Introduced: 7 May 2026
CVE-2026-44513 (opens in a new tab)How to fix?
Upgrade diffusers to version 0.38.0 or higher.
Overview
diffusers is a State-of-the-art diffusion in PyTorch and JAX.
Affected versions of this package are vulnerable to Arbitrary Code Injection in the from_pretrained fucntion when a repository contains a None.py file and the custom_pipeline argument is not supplied. An attacker can execute arbitrary code by uploading a malicious None.py file to a model repository and convincing a user to load the model without specifying custom_pipeline or trust_remote_code arguments.
Note:
CVE-2026-44827 is a duplicate of this advisory.
Workaround
This vulnerability can be mitigated by only calling the loading process with model sources that are fully trusted and have been audited, and by inspecting local snapshots for unexpected Python files before use.