Despite substantial progress in safety alignment, aligned Large Language Models (LLMs) remain fragile under small parameter-space and activation-space perturbations, such as downstream fine-tuning and jailbreak attacks. To explain why local perturbations can trigger broad safety failures, we analyze safety-task neuron circuits from the perspectives of activation flow and circuit topology. We identify a critical topological fragility in safety alignment: In aligned models, safety-task neuron circuits exhibit a low-redundancy, isolated single-chain topology, in sharp contrast to the highly redundant dense-mesh topology of general-task circuits. This brittle topology creates rigid serial dependencies, whereby perturbing a small set of upstream neurons can disrupt downstream safety neurons and cascade into broad alignment failures. To mitigate this topological fragility, we propose Dense Safety Mesh (DSM), a topology-aware alignment framework that transforms brittle single-chain safety circuits into more redundant and resilient mesh-like structures. DSM employs structural adversarial masking to suppress dominant safety pathways, forcing safety circuits to develop diverse parallel pathways. Extensive experiments across seven models and multiple evaluations show that DSM improves robustness against perturbations attacks while maintaining general capabilities.