🚀 LLM4Decompile项目

LLM4Decompile旨在将x86汇编指令反编译为C语言代码。新发布的V2系列使用更大的数据集（2B标记）进行训练，最大标记长度为4096，与之前的模型相比，性能有显著提升（最高可达100%）。

🚀 快速开始

项目仓库

• Github仓库：LLM4Decompile

✨ 主要特性

LLM4Decompile的主要特性在于其强大的反编译能力，尤其是新发布的V2系列，通过使用更大的数据集和更长的最大标记长度，在将x86汇编指令反编译为C语言代码方面表现出色，相比之前的模型有显著的性能提升。

📦 安装指南

1. 安装Ghidra

下载 Ghidra 到当前文件夹。你也可以查看 此页面 获取其他版本。将压缩包解压到当前文件夹。 在bash中，你可以使用以下命令：

cd LLM4Decompile/ghidra
wget https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip
unzip ghidra_11.0.3_PUBLIC_20240410.zip

2. 安装Java-SDK-17

Ghidra 11依赖于Java-SDK-17，在Ubuntu上安装SDK的简单方法如下：

apt-get update
apt-get upgrade
apt install openjdk-17-jdk openjdk-17-jre

请查看 Ghidra安装指南 以获取其他平台的安装方法。

💻 使用示例

基础用法

3. 使用Ghidra Headless反编译二进制文件（demo.py）

注意：将 func0 替换为你要反编译的函数名。 预处理：将C代码编译为二进制文件，并将二进制文件反汇编为汇编指令。

import os
import subprocess
from tqdm import tqdm,trange

OPT = ["O0", "O1", "O2", "O3"]
timeout_duration = 10

ghidra_path = "./ghidra_11.0.3_PUBLIC/support/analyzeHeadless"#path to the headless analyzer, change the path accordingly
postscript = "./decompile.py"#path to the decompiler helper function, change the path accordingly
project_path = "."#path to temp folder for analysis, change the path accordingly
project_name = "tmp_ghidra_proj"
func_path = "../samples/sample.c"#path to c code for compiling and decompiling, change the path accordingly
fileName = "sample"

with tempfile.TemporaryDirectory() as temp_dir:
    pid = os.getpid()
    asm_all = {}
    for opt in [OPT[0]]:
        executable_path = os.path.join(temp_dir, f"{pid}_{opt}.o")
        cmd = f'gcc -{opt} -o {executable_path} {func_path} -lm'
        subprocess.run(
        cmd.split(' '),
        check=True,
        stdout=subprocess.DEVNULL,  # Suppress stdout
        stderr=subprocess.DEVNULL,  # Suppress stderr
        timeout=timeout_duration,
        )

        output_path = os.path.join(temp_dir, f"{pid}_{opt}.c")
        command = [
            ghidra_path,
            temp_dir,
            project_name,
            "-import", executable_path,
            "-postScript", postscript, output_path,
            "-deleteProject",  # WARNING: This will delete the project after analysis
        ]
        result = subprocess.run(command, text=True, capture_output=True, check=True)
        with open(output_path,'r') as f:
            c_decompile = f.read()
        c_func = []
        flag = 0
        for line in c_decompile.split('\n'):
            if "Function: func0" in line:#**Replace** func0 with the function name you want to decompile.
                flag = 1
                c_func.append(line)
                continue
            if flag:
                if '// Function:' in line:
                    if len(c_func) > 1:
                        break
                c_func.append(line)
        if flag == 0:
            raise ValueError('bad case no function found')
        for idx_tmp in range(1,len(c_func)):##########remove the comments
            if 'func0' in c_func[idx_tmp]:
                break
        c_func = c_func[idx_tmp:]
        input_asm = '\n'.join(c_func).strip()

        before = f"# This is the assembly code:\n"#prompt
        after = "\n# What is the source code?\n"#prompt
        input_asm_prompt = before+input_asm.strip()+after
        with open(fileName +'_' + opt +'.pseudo','w',encoding='utf-8') as f:
            f.write(input_asm_prompt)

Ghidra伪代码可能如下所示：

undefined4 func0(float param_1,long param_2,int param_3)
{
  int local_28;
  int local_24;
  
  local_24 = 0;
  do {
    local_28 = local_24;
    if (param_3 <= local_24) {
      return 0;
    }
    while (local_28 = local_28 + 1, local_28 < param_3) {
      if ((double)((ulong)(double)(*(float *)(param_2 + (long)local_24 * 4) -
                                  *(float *)(param_2 + (long)local_28 * 4)) &
                  SUB168(_DAT_00402010,0)) < (double)param_1) {
        return 1;
      }
    }
    local_24 = local_24 + 1;
  } while( true );
}

4. 使用LLM4Decompile优化伪代码（demo.py）

反编译：使用LLM4Decompile-Ref将Ghidra伪代码优化为C语言代码：

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = 'LLM4Binary/llm4decompile-6.7b-v2' # V2 Model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()

with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#optimization level O0
    asm_func = f.read()
inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])

with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#original file
    func = f.read()

print(f'pseudo function:\n{func}')# Note we only decompile one function, where the original file may contain multiple functions
print(f'refined function:\n{c_func_decompile}')

📚 详细文档

评估结果

📄 许可证

本代码仓库遵循MIT许可证。

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