RASA：面向混合专家模型的路由感知安全对齐
Routing-Aware Safety Alignment for Mixture-of-Experts Models
AI总结：
RASA通过针对性修复安全关键专家提升混合专家模型的安全性，有效防止路由绕过，实现高鲁棒性和跨攻击泛化能力。
摘要：
混合专家（MoE）语言模型由于其稀疏路由机制，在标准全参数微调下可能产生退化优化行为。本文提出RASA，一种路由感知的专家级对齐框架，通过显式修复安全关键专家并防止路由绕过，实现了在两种代表性MoE架构和多种 jailbreak 攻击下的近完美鲁棒性、强跨攻击泛化能力和显著降低的过度拒绝率，同时在MMLU、GSM8K和TruthfulQA等基准上保持一般能力。结果表明，鲁棒的MoE安全对齐受益于针对性专家修复而非全局参数更新，提供了一种实用且架构保持的替代方案。
英文摘要：
Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable degenerate optimization behaviors under standard full-parameter fine-tuning. In our preliminary experiments, we observe that naively applying full-parameter safety fine-tuning to MoE models can reduce attack success rates through routing or expert dominance effects, rather than by directly repairing Safety-Critical Experts. To address this challenge, we propose RASA, a routing-aware expert-level alignment framework that explicitly repairs Safety-Critical Experts while preventing routing-based bypasses. RASA identifies experts disproportionately activated by successful jailbreaks, selectively fine-tunes only these experts under fixed routing, and subsequently enforces routing consistency with safety-aligned contexts. Across two representative MoE architectures and a diverse set of jailbreak attacks, RASA achieves near-perfect robustness, strong cross-attack generalization, and substantially reduced over-refusal, while preserving general capabilities on benchmarks such as MMLU, GSM8K, and TruthfulQA. Our results suggest that robust MoE safety alignment benefits from targeted expert repair rather than global parameter updates, offering a practical and architecture-preserving alternative to prior approaches.
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