Researchers propose a novel threat to large language models: One-sided Merging-Conditional Backdoor (OMCB), which remains dormant and undetectable until models are merged, causing safety unalignment. They introduce SMOKE, a meta-learning-based attack framework that implants OMCB and demonstrate its effectiveness across various models and merging techniques.