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ai security
Hugging Face Diffusers Flaws Could Let Model Repositories Execute Arbitrary Code
Three high-severity vulnerabilities in Hugging Face's Diffusers library allow malicious model repositories to execute arbitrary code by circumventing the trust_remote_code safety mechanism. These flaws expose the AI supply chain to unauthorized code execution when users load affected models.
Why it matters: ML practitioners and organizations using Hugging Face models face remote code execution risk; patching and code review of model sources are now critical to prevent supply chain compromise.
- Source published
- First seen by Cybersecurity Tracker
Source attribution
Correction
Correction recorded as of .
ai security
Hugging Face Diffusers Flaws Could Let Model Repositories Execute Arbitrary Code
No summary had been written when this copy was frozen.
- First seen by Cybersecurity Tracker
Source attribution
Correction
Correction recorded as of .
ai security
Hugging Face Diffusers Flaws Could Let Model Repositories Execute Arbitrary Code
Three high-severity vulnerabilities in Hugging Face's Diffusers library could permit malicious model repositories to execute arbitrary code on systems that load them, circumventing the trust_remote_code safety mechanism intended to block unreviewed code execution.
Why it matters: Machine learning practitioners and organizations using Hugging Face models face supply chain risk if they load compromised repositories; patching Diffusers and validating model sources becomes urgent.
- Source published
- First seen by Cybersecurity Tracker