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Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation

NeurIPS 2026 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Boundedly rational agent modeling · 0 citations (OpenAlex)

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Shows that a deletion bias (API setting) and a chmod-first bias (Bash setting) transfer subliminally from a LoRA-fine-tuned teacher to a student distilled only on keyword-filtered safe trajectories, with students reaching up to 100% deletion rate.

Problem

Whether behavioral (action-level) unsafe traits in LLM agents can transfer subliminally through distillation on filtered, ostensibly safe trajectories, which keyword sanitation would not catch.

Approach

Induce a destructive or permission-related bias in a teacher agent via LoRA fine-tuning, have it generate trajectories on safe tasks, strip all deletion-related keywords, and distill into a student; measure the student's propensity on ambiguous tasks against an unbiased baseline and a random-task control. Replicates the setting in a native Bash environment with a chmod-first metric.

Key results

• Llama 8B to Llama 8B distillation raised deletion rate from a 5% baseline to 100% in the API setting (+95pp), while a random-task control reached only +20pp.
• Llama 8B to Qwen 7B transferred deletion bias to 100% from a 20% baseline (+80pp) in the API setting.
• In the Bash setting, chmod-first rate for Llama 8B to Llama 3B reached 55% vs a 10% baseline (+45pp); Llama 3B to Llama 8B showed no transfer (5%).

Surprising findings

• Transfer occurred despite keyword filtering of all deletion-related tokens from student training data.
• Small-to-large distillation (3B to 8B) produced no measurable transfer in either setting.

Limitations

• Only 20 ambiguous evaluation tasks, limiting statistical precision; the paper itself acknowledges this.
• Single unsafe behavior per setting and synthetic task environments.
• Mechanism of transfer not identified; limited model family coverage.

Experimental setup

Datasets: teacher deletion task set (150 task-plan pairs) safe trajectory set (400 trajectories, keyword-filtered) ambiguous evaluation set (20 tasks)

Benchmarks: ambiguous evaluation set (20 tasks, API setting) Bash chmod-first evaluation tasks

Metrics: deletion propensity deletion rate chmod-first rate percentage point increase over baseline

Baselines: unbiased base model random-task control student

Builds on: subliminal learning

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