Adapting large language models (LLMs) to domain-specific tasks via fine-tuning is often infeasible: models are protected by intellectual property, while sensitive data cannot be shared due to privacy regulations. A promising paradigm, Offsite Tuning (OT), addresses this challenge by constructing an emulator of the original model. Data owners leverage the emulator to train an adapter on downstream data, which is then plugged back into the original model, enabling knowledge transfer without transmitting either the original model or the raw data. However, emulators constructed by existing OT-based methods often retain substantial inference capabilities, thereby exposing model capability privacy and posing risks of misuse. To address this, we propose Loss Landscape Elevation Offsite Tuning (LLEOT), a framework that secures data privacy as well as model parameter and capability privacy. At its core, Loss Landscape Elevation (LLE) enforces a fixed margin between the loss landscapes of the emulator and the original model. We theoretically demonstrate that LLE simultaneously (i) degrades emulator inference via perplexity amplification and (ii) preserves gradient alignment, ensuring consistent convergence for adapter training. Extensive experiments confirm that LLEOT achieves strong adaptation performance while effectively mitigating emulator misuse. Code is available at https://github.com/Z-eloto/LLEOT.