103 lines
4.5 KiB
Markdown
103 lines
4.5 KiB
Markdown
---
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language: code # <-- my language
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widget:
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- text: "# This is the assembly code with O0 optimization:\n<func0>:\nendbr64\nlea (%rdi,%rsi,1),%eax\nretq\n# What is the source code?\n"
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license: other
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tags:
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- decompile
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- binary
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---
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### 1. Introduction of LLM4Decompile
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LLM4Decompile aims to decompile x86 assembly instructions into C. It is finetuned from Deepseek-Coder on 4B tokens of assembly-C pairs compiled from AnghaBench.
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- **Github Repository:** [LLM4Decompile](https://github.com/albertan017/LLM4Decompile)
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- **Paper link:** For more details check out the [paper](https://arxiv.org/abs/2403.05286).
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### 2. Evaluation Results
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| Model | Re-compilability | | | | | Re-executability | | | | |
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|--------------------|:----------------:|:---------:|:---------:|:---------:|:---------:|:----------------:|-----------|-----------|-----------|:---------:|
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| Optimization-level | O0 | O1 | O2 | O3 | Avg. | O0 | O1 | O2 | O3 | Avg. |
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| GPT4 | 0.92 | 0.94 | 0.88 | 0.84 | 0.895 | 0.1341 | 0.1890 | 0.1524 | 0.0854 | 0.1402 |
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| DeepSeek-Coder-33B | 0.0659 | 0.0866 | 0.1500 | 0.1463 | 0.1122 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
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| LLM4Decompile-1b | 0.8780 | 0.8732 | 0.8683 | 0.8378 | 0.8643 | 0.1573 | 0.0768 | 0.1000 | 0.0878 | 0.1055 |
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| LLM4Decompile-6b | 0.8817 | 0.8951 | 0.8671 | 0.8476 | 0.8729 | 0.3000 | 0.1732 | 0.1988 | 0.1841 | 0.2140 |
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| LLM4Decompile-33b | 0.8134 | 0.8195 | 0.8183 | 0.8305 | 0.8204 | 0.3049 | 0.1902 | 0.1817 | 0.1817 | 0.2146 |
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### 3. How to Use
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Here give an example of how to use our model.
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First compile the C code into binary, disassemble the binary into assembly instructions:
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```python
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import subprocess
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import os
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import re
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digit_pattern = r'\b0x[a-fA-F0-9]+\b'# binary codes in Hexadecimal
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zeros_pattern = r'^0+\s'#0s
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OPT = ["O0", "O1", "O2", "O3"]
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fileName = 'path/to/file'
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with open(fileName+'.c','r') as f:#original file
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c_func = f.read()
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for opt_state in OPT:
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output_file = fileName +'_' + opt_state
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input_file = fileName+'.c'
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compile_command = f'gcc -c -o {output_file}.o {input_file} -{opt_state} -lm'#compile the code with GCC on Linux
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subprocess.run(compile_command, shell=True, check=True)
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compile_command = f'objdump -d {output_file}.o > {output_file}.s'#disassemble the binary file into assembly instructions
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subprocess.run(compile_command, shell=True, check=True)
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input_asm = ''
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with open(output_file+'.s') as f:#asm file
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asm= f.read()
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asm = asm.split('Disassembly of section .text:')[-1].strip()
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for tmp in asm.split('\n'):
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tmp_asm = tmp.split('\t')[-1]#remove the binary code
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tmp_asm = tmp_asm.split('#')[0].strip()#remove the comments
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input_asm+=tmp_asm+'\n'
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input_asm = re.sub(zeros_pattern, '', input_asm)
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before = f"# This is the assembly code with {opt_state} optimization:\n"#prompt
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after = "\n# What is the source code?\n"#prompt
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input_asm_prompt = before+input_asm.strip()+after
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with open(fileName +'_' + opt_state +'.asm','w',encoding='utf-8') as f:
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f.write(input_asm_prompt)
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```
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Then use LLM4Decompile to translate the assembly instructions into C:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_path = 'arise-sustech/llm4decompile-1.3b'
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16).cuda()
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with open(fileName +'_' + opt_state +'.asm','r') as f:#original file
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asm_func = f.read()
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inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512)
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c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
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```
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### 4. License
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This code repository is licensed under the DeepSeek License.
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### 5. Contact
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If you have any questions, please raise an issue.
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### 6. Citation
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```
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@misc{tan2024llm4decompile,
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title={LLM4Decompile: Decompiling Binary Code with Large Language Models},
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author={Hanzhuo Tan and Qi Luo and Jing Li and Yuqun Zhang},
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year={2024},
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eprint={2403.05286},
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archivePrefix={arXiv},
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primaryClass={cs.PL}
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}
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``` |