Forking is a typical way of code reuse, which provides a simple way for developers to create a variant software (denoted as hard fork) by copying and modifying an existing codebase. Despite of the benefits, forking also leads to duplicate efforts in software maintenance. Developers need to port patches across the hard forks to address similar bugs or implement similar features. Due to the divergence between the source project and the hard fork, patch porting is complicated, which requires an adaption regarding different implementations of the same functionality. In this work, we take the first step to automate patch porting for hard forks under a zero-shot setting. We first conduct an empirical study of the patches ported from Vim to Neovim over the last ten years to investigate the necessities of patch porting and the potential flaws in the current practice. We then propose a large language model (LLM) based approach (namely PPatHF) to automatically port patches for hard forks on a function-wise basis. Specifically, PPatHF is composed of a reduction module and a porting module. Given the pre- and post-patch versions of a function from the reference project and the corresponding function from the target project, the reduction module first slims the input functions by removing code snippets less relevant to the patch. Then, the porting module leverages a LLM to apply the patch to the function from the target project. To better elicit the power of the LLM on patch porting, we design a prompt template to enable efficient in-context learning. We further propose an instruction-tuning based training task to better guide the LLM to port the patch and inject task-specific knowledge. We evaluate PPatHF on 310 Neovim patches ported from Vim. The experimental results show that PPatHF outperforms the baselines significantly. Specifically, PPatHF can correctly port 131 (42.3%) patches and automate 57% of the manual edits required for the developer to port the patch.