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This critical vulnerability in Hugging Face Transformers exposes a race condition where malicious code is cached on local systems before the user's explicit trust confirmation is processed. As the library underpins much of the natural language processing and multimodal AI ecosystem, this flaw allows attackers to compromise developer workstations and production ML pipelines without triggering standard safety prompts. Immediate patching is essential to prevent unauthorized Python execution from untrusted model repositories.
Hugging Face Flaw Lets Malicious AI Models Plant Python Code on User Systems
A newly disclosed vulnerability in Hugging Face Transformers could allow malicious AI model repositories to place attacker-controlled Python files on a user’s system before the user approves remote code execution. Tracked as CVE-2026-80047, the issue affects Hugging Face Transformers versions 4.49.0 through 5.8.1. The flaw lies in the library’s custom generation-loading process. It can allow […] The post Hugging Face Flaw Lets Malicious AI Models Plant Python Code on User Systems appeared first on Cyber Security News.
A newly disclosed vulnerability in Hugging Face Transformers could allow malicious AI model repositories to place attacker-controlled Python files on a user’s system before the user approves remote code execution. Tracked as CVE-2026-80047, the issue affects Hugging Face Transformers versions 4.49.0 through 5.8.1. The flaw lies in the library’s custom generation-loading process. It can…
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