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| Metrics | Re-executability Rate | Edit Similarity | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Optimization Level | O0 | O1 | O2 | O3 | AVG | O0 | O1 | O2 | O3 | AVG |
| LLM4Decompile-End-6.7B | 0.6805 | 0.3951 | 0.3671 | 0.3720 | 0.4537 | 0.1557 | 0.1292 | 0.1293 | 0.1269 | 0.1353 |
| Ghidra | 0.3476 | 0.1646 | 0.1524 | 0.1402 | 0.2012 | 0.0699 | 0.0613 | 0.0619 | 0.0547 | 0.0620 |
| +GPT-4o | 0.4695 | 0.3415 | 0.2866 | 0.3110 | 0.3522 | 0.0660 | 0.0563 | 0.0567 | 0.0499 | 0.0572 |
| +LLM4Decompile-Ref-1.3B | 0.6890 | 0.3720 | 0.4085 | 0.3720 | 0.4604 | 0.1517 | 0.1325 | 0.1292 | 0.1267 | 0.1350 |
| +LLM4Decompile-Ref-6.7B | 0.7439 | 0.4695 | 0.4756 | 0.4207 | 0.5274 | 0.1559 | 0.1353 | 0.1342 | 0.1273 | 0.1382 |
| +LLM4Decompile-Ref-33B | 0.7073 | 0.4756 | 0.4390 | 0.4146 | 0.5091 | 0.1540 | 0.1379 | 0.1363 | 0.1307 | 0.1397 |
1cd LLM4Decompile/ghidra
2wget https://github.com/NationalSecurityAgency/ghidra/releases/download/Ghidra_11.0.3_build/ghidra_11.0.3_PUBLIC_20240410.zip
3unzip ghidra_11.0.3_PUBLIC_20240410.zip1apt-get update
2apt-get upgrade
3apt install openjdk-17-jdk openjdk-17-jre1import os
2import subprocess
3from tqdm import tqdm,trange
4
5OPT = ["O0", "O1", "O2", "O3"]
6timeout_duration = 10
7
8ghidra_path = "./ghidra_11.0.3_PUBLIC/support/analyzeHeadless"#path to the headless analyzer, change the path accordingly
9postscript = "./decompile.py"#path to the decompiler helper function, change the path accordingly
10project_path = "."#path to temp folder for analysis, change the path accordingly
11project_name = "tmp_ghidra_proj"
12func_path = "../samples/sample.c"#path to c code for compiling and decompiling, change the path accordingly
13fileName = "sample"
14
15with tempfile.TemporaryDirectory() as temp_dir:
16 pid = os.getpid()
17 asm_all = {}
18 for opt in [OPT[0]]:
19 executable_path = os.path.join(temp_dir, f"{pid}_{opt}.o")
20 cmd = f'gcc -{opt} -o {executable_path} {func_path} -lm'
21 subprocess.run(
22 cmd.split(' '),
23 check=True,
24 stdout=subprocess.DEVNULL, # Suppress stdout
25 stderr=subprocess.DEVNULL, # Suppress stderr
26 timeout=timeout_duration,
27 )
28
29 output_path = os.path.join(temp_dir, f"{pid}_{opt}.c")
30 command = [
31 ghidra_path,
32 temp_dir,
33 project_name,
34 "-import", executable_path,
35 "-postScript", postscript, output_path,
36 "-deleteProject", # WARNING: This will delete the project after analysis
37 ]
38 result = subprocess.run(command, text=True, capture_output=True, check=True)
39 with open(output_path,'r') as f:
40 c_decompile = f.read()
41 c_func = []
42 flag = 0
43 for line in c_decompile.split('\n'):
44 if "Function: func0" in line:#**Replace** func0 with the function name you want to decompile.
45 flag = 1
46 c_func.append(line)
47 continue
48 if flag:
49 if '// Function:' in line:
50 if len(c_func) > 1:
51 break
52 c_func.append(line)
53 if flag == 0:
54 raise ValueError('bad case no function found')
55 for idx_tmp in range(1,len(c_func)):##########remove the comments
56 if 'func0' in c_func[idx_tmp]:
57 break
58 c_func = c_func[idx_tmp:]
59 input_asm = '\n'.join(c_func).strip()
60
61 before = f"# This is the assembly code:\n"#prompt
62 after = "\n# What is the source code?\n"#prompt
63 input_asm_prompt = before+input_asm.strip()+after
64 with open(fileName +'_' + opt +'.pseudo','w',encoding='utf-8') as f:
65 f.write(input_asm_prompt)1undefined4 func0(float param_1,long param_2,int param_3)
2{
3 int local_28;
4 int local_24;
5
6 local_24 = 0;
7 do {
8 local_28 = local_24;
9 if (param_3 <= local_24) {
10 return 0;
11 }
12 while (local_28 = local_28 + 1, local_28 < param_3) {
13 if ((double)((ulong)(double)(*(float *)(param_2 + (long)local_24 * 4) -
14 *(float *)(param_2 + (long)local_28 * 4)) &
15 SUB168(_DAT_00402010,0)) < (double)param_1) {
16 return 1;
17 }
18 }
19 local_24 = local_24 + 1;
20 } while( true );
21}1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_path = 'LLM4Binary/llm4decompile-6.7b-v2' # V2 Model
5tokenizer = AutoTokenizer.from_pretrained(model_path)
6model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()
7
8with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#optimization level O0
9 asm_func = f.read()
10inputs = tokenizer(asm_func, return_tensors="pt").to(model.device)
11with torch.no_grad():
12 outputs = model.generate(**inputs, max_new_tokens=2048)### max length to 4096, max new tokens should be below the range
13c_func_decompile = tokenizer.decode(outputs[0][len(inputs[0]):-1])
14
15with open(fileName +'_' + OPT[0] +'.pseudo','r') as f:#original file
16 func = f.read()
17
18print(f'pseudo function:\n{func}')# Note we only decompile one function, where the original file may contain multiple functions
19print(f'refined function:\n{c_func_decompile}')
20