New Hugging Face Vulnerability Exposes AI Models to Supply Chain Attacks


Cybersecurity researchers have found that it’s possible to compromise the Hugging Face Safetensors conversion service to ultimately hijack the models submitted by users and result in supply chain attacks.

This, in turn, can be accomplished using a hijacked model that’s meant to be converted by the service, thereby allowing malicious actors to request changes to any repository on the platform by masquerading as the conversion bot.

Hugging Face is a popular collaboration platform that helps users host pre-trained machine learning models and datasets, as well as build, deploy, and train them.

HiddenLayer’s analysis of this module found that it’s hypothetically possible for an attacker to hijack the hosted conversion service using a malicious PyTorch binary and compromise the system hosting it.

“An attacker could gain a foothold into the container running the service and compromise any model converted by the service.”

The development comes a little over a month after Trail of Bits disclosed LeftoverLocals (CVE-2023-4969, CVSS score: 6.5), a vulnerability that allows recovery of data from Apple, Qualcomm, AMD, and Imagination general-purpose graphics processing units (GPGPUs).

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