Dense retrievers are a core component of Retrieval-Augmented Generation (RAG) systems, yet their growing adoption introduces critical security risks in the Al supply chain. Existing retrieval backdoor attacks primarily rely on coarse-grained parameter updates, which often degrade benign retrieval behavior and require substantial computational cost, limiting their stealth and practicality across deployment settings. In this paper, we propose LOKI (Locating Optimal Knock-In), a framework that reformulates stealthy retrieval backdoor injection as a neuron-level local editing problem. Following a Locate-then-Edit paradigm, LOKI first identifies pivotal neurons that are highly sensitive to triggers yet decoupled from benign retrieval semantics via a multi-dimensional diagnostic analysis. And then LOKI implants backdoor logic through constrained local editing that balances trigger-induced alignment with semantic manifold preservation. Extensive experiments show that LOKI achieves near 100% attack success while modifying fewer than 1% of model parameters, with only limited impact on clean retrieval performance.