As the pretraining-finetuning paradigm becomes dominant, it exposes new vulnerabilities in the model supply chain, particularly through sophisticated backdoor attacks. Prevailing research has largely focused on backdoors embedded during pretraining, viewing subsequent finetuning merely as a potential defense. This perspective overlooks the possibility of weaponizing the finetuning process itself, leaving a critical security blind spot. While emerging studies have explored finetuning-activated backdoors, their efficacy critically depends on white-box access to the downstream task's data distribution. This reliance on unobtainable prior knowledge severely limits their real-world feasibility. In this work, we propose the Dormant Backdoor, \textbf{a novel backdoor attack robust across unknown downstream tasks by weaponizing the finetuning process itself}. The key innovation is to shift the trigger from static data features to the universal dynamics of gradient-based optimization. We engineer the backdoor to be dormant and stealthy in the pretrained model, making it indistinguishable from a benign one. During finetuning, however, the very gradient updates intended for task adaptation are hijacked to progressively awaken and amplify the malicious functionality, turning the learning process against itself. Our comprehensive evaluations across multiple downstream datasets, finetuning techniques and backdoor detection schemes demonstrate that Dormant Backdoor persists reliably, revealing a new and dangerous class of process-as-trigger vulnerabilities inherent in the modern AI ecosystem.