---
license: mit
---
SK²Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin

SK²Decompile is a novel two-phase framework for binary decompilation using Large Language Models (LLMs). Our approach decomposes the complex decompilation task into two manageable phases:

Phase 1 Structure Recovery (Skeleton): Transform binary/pseudo-code into obfuscated intermediate representations (current model)

Phase 2 Identifier Naming (Skin): Generate human-readable source code with meaningful identifiers 🤗 [HF Link](https://huggingface.co/LLM4Binary/sk2decompile-ident-6.7)

Usage:

0. Install `vllm` and `transformers` via `pip`; install `clang-format` via `apt`.
1. Prepare a Linux-x64 executable file (ELF).
2. Use IDA to decompile it (you can also simply use this website: [https://dogbolt.org/](https://dogbolt.org/)).
3. Convert the data into the corresponding format
   ([https://huggingface.co/LLM4Binary/sk2decompile-struct-6.7b/blob/main/reverse_sample.json](https://huggingface.co/LLM4Binary/sk2decompile-struct-6.7b/blob/main/reverse_sample.json))
  ```bash
  python normalize_pseudo.py --input_json reverse_sample.json --output_json reverse_sample.json
  ```
4. Run inference:

   ```bash
   python sk2decompile.py --dataset_path reverse_sample.json \
       --model_path LLM4Binary/sk2decompile-struct-6.7b \
       --recover_model_path LLM4Binary/sk2decompile-ident-6.7b
   ```

**Project overview:**
[https://github.com/albertan017/LLM4Decompile/tree/main/sk2decompile](https://github.com/albertan017/LLM4Decompile/tree/main/sk2decompile)

**Notes:**

* IDA decompilation results should be preprocessed before inference.
* Use `vllm` to recover function structure (`sk2decompile-struct`) and variable names (`sk2decompile-ident`) step by step.
* Training was done on C language Linux-x64 code with IDA pseudocode; performance may degrade for other languages or architectures.