Pretraining poison can backdoor Qwen3 CLI agents for RCE
Original title: Agentic Backdoors via Pretraining Poisoning
Poisoning 0.1% of pretraining data led the 4B agent to about 92% attack success, and success stayed positive at 0.0005%.
Researchers report that poisoned public pretraining text can implant a remote code execution backdoor in a later CLI agent. They pretrained Qwen3 models from scratch at 0.6B, 1.7B, and 4B, then post-trained each into an agent with a clean pipeline. With 0.1% of the corpus poisoned, the deployed agent ran an attacker-chosen command when a trigger was present and behaved normally on clean inputs. Attack success rose with model scale. Code is released at a public safety-research repository.
Unconfirmed: Attack success rates are the authors’ experimental results, not evidence of poisoning in a deployed model.
- Models were pretrained from scratch at 0.6B, 1.7B, and 4B.
- Post-training into the CLI agent used a clean pipeline.
- The 4B model reached roughly 92% ASR at a 0.1% poison rate.
- ASR remained positive when the poison rate fell to 0.0005%.
From the original article
1 min read at arxiv.orgLarge language models are pretrained on vast, uncurated corpora scraped from the public web. This creates a security risk from attackers who plant poisoned content in the pretraining data. We show for the first time that such poisoning can plant a remote code execution backdoor in an agent, causing it to execute attacker-chosen commands on the user's machine.
By Xinyan Hu · arxiv.org
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