Large language model unlearning aims to suppress designated knowledge without degrading retained capabilities, yet existing approaches often fall into destructive over-suppression or conservative under-suppression. We introduce protected update routing, an intervention-guided framework that controls where forgetting updates flow within LoRA adapters. The method activates measurable adapter paths, identifies forget-specific rows from gradient attribution, intervention-induced loss changes, and forget–retain specificity, and applies localized suppression under retain-aware masking and stability safeguards. Multi-seed experiments on TOFU and WMDP show that protected routing provides a controllable forgetting–utility trade-off: it strengthens target suppression over NPO and RMU on TOFU while retaining higher downstream utility on WMDP. In a protocol-matched comparison with PALU, it achieves higher model utility at a similar forget-quality level. Analyses across MUSE-News, alternative routing granularities, and larger models further support the effectiveness and interpretability of separating route selection from update strength. Protected update routing therefore offers a practical and diagnosable interface for parameter-efficient unlearning.