HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models

Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a single global prompt or externally selected prompt modules. Such static designs struggle to maintain a cross-category safety boundary while generating constructive responses tailored to specific risks and avoiding over-refusal of benign inputs. To address these limitations, we propose HiRoute, an input-adaptive hierarchi

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ethics.ai (13 August 2026), “HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models,” evidence record 19472, https://ethics.ai/record/19472 (originally published by arXiv red teaming query).

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