Exploiting Pretraining Data Poisoning to Embed Agentic Backdoors in Large Language Models

This paper demonstrates how pretraining data poisoning can implant remote code execution backdoors in large language models, enabling attackers to execute arbitrary commands on user machines. The attack achieves high success rates (up to 92%) even with minimal poisoning rates (0.1%) across different model sizes.

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