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.

Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness. However, we observe that a too large number of search steps can hurt accuracy. We aim to obtain strong robustness efficiently using fewer steps. Through a toy experiment, we find that perturbing the clean data to the decision boundary but not crossing it does not degrade the test accuracy. Inspired by this, we propose friendly adversarial data augmentation (FADA) to generate friendly adversarial data. On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps. Comprehensive experiments across two widely used datasets and three pre-trained language models demonstrate that GAT can obtain stronger robustness via fewer steps. In addition, we provide extensive empirical results and in-depth analyses on robustness to facilitate future studies.