New research
Researchers Poisoned 0.1% of a Model's Training Data and Planted a Backdoor That Makes Its Coding Agent Run Their Commands
A paper posted to arXiv on 27 October says it shows for the first time that poisoned web text in pretraining data can plant a remote-code-execution backdoor in an AI agent. The authors pretrained Qwen3 models from scratch at 0.6B, 1.7B and 4B parameters, turned each into a command-line agent with a clean post-training pipeline, and report that the agent ran the attacker's command when a trigger appeared while behaving normally otherwise. The poison went into the reading list, which is the one part of the job nobody proofreads.
Key facts
- At a 0.1% poisoning rate the 4B model executed the malicious command roughly 92% of the time, according to the abstract.
- The attack success rate rose with model size and stayed above zero even at a poisoning rate of 0.0005%.
- The backdoored agents are models the researchers trained themselves; the abstract does not report finding such a backdoor in any commercial model.
- The authors published their code on GitHub, and the paper drew discussion on Hacker News on 2 November.
Sources
- Original reportarXiv: Agentic Backdoors via Pretraining Poisoning arxiv.org
- Also reported byGitHub: the paper's code github.com
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