Researchers Demonstrate Remote Code Execution Backdoors in LLM Agents via Pretraining Poisoning

·2026.11.03 03:30

Key point

Poisoning just 0.1% of the pretraining corpus for Qwen3 models implants a backdoor that allows agents to execute attacker-chosen commands on user machines.

Details

First demonstration that pretraining data poisoning can implant a remote code execution backdoor in LLM agents, causing them to run malicious commands on user hardware.
The attack achieves roughly 92% success rate on 4B parameter models with only 0.1% of the pretraining corpus poisoned, and remains effective even at a 0.0005% poisoning rate.
Experiments involved pretraining Qwen3 models from scratch at 0.6B, 1.7B, and 4B sizes, followed by post-training into CLI agents using a clean pipeline.
Attack success rate increases with model scale, and the code for the poison design is available on GitHub.

llm-security qwen3 ai-agents remote-code-execution arxiv pretraining-poisoning

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