# LLM Agent Safety

Tool-using language model agents can refuse one phrasing of a harmful request and comply with another, and a missed refusal becomes an action. Llama-3.2-1B-Instruct was fine-tuned with LoRA on data not drawn from AgentHarm, comparing latent attribution regularization (LAR) against an augmentation-only control over three seeds. At 10% over-refusal on harmless tool tasks, LAR lowered the share of tasks with mixed refusal decisions from 25% to 19% and paraphrase-induced flips from 11.3% to 9.3%, while harmful-versus-harmless detection did not change significantly.

Tool-using language model agents can refuse one phrasing of a harmful request and comply with another, and a missed refusal becomes an action. Llama-3.2-1B-Instruct was fine-tuned with LoRA on data not drawn from AgentHarm, comparing latent attribution regularization (LAR) against an augmentation-only control over three seeds. At 10% over-refusal on harmless tool tasks, LAR lowered the share of tasks with mixed refusal decisions from 25% to 19% and paraphrase-induced flips from 11.3% to 9.3%, while harmful-versus-harmless detection did not change significantly.