The Sleeper in the Weights: We Planted a Backdoor in an Agent, and Only the Kernel Caught It

A VIDRAFT article discusses research showing that small amounts of poisoned pretraining data can implant command-execution backdoors in AI agents that persist through later safety training and activate from seemingly ordinary file-path triggers. The authors report a separate fine-tuning experiment on Qwen3-0.6B in which a backdoor activated on most unseen trigger paths, generalized to related words, evaded 24 standard behavioral checks, and left normal task performance intact. They argue that detecting unknown triggers is unreliable and instead advocate controlling an agent’s actions immediately before execution. Their ViKernel system embeds a rule engine, kill switch, and tamper-evident audit mechanism within model weights; in a read-only demonstration policy, it reportedly blocked all 290 proposed malicious shell payloads while allowing permitted read-only commands, though it also denied benign write operations outside the narrow policy. The article acknowledges that its experiment used fine-tuning rather than web-scale pretraining poisoning, that the system does not address harmful behavior conducted through allowed actions, and that it should serve as one layer in a broader defense-in-depth approach.

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