ChatGPT在油井白盒代码审计中的应用

pythonCopy codefrom transformers import GPT2LMHeadModel, GPT2Tokenizer
# 加载ChatGPT模型和Tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 准备训练数据
train_dataset = [
    {
        'code': '...',
        'vulnerability': '...'
    },
    ...
]
# 对训练数据进行处理
train_inputs = []
train_labels = []
for example in train_dataset:
    code = example['code']
    vulnerability = example['vulnerability']
    # 将问答对编码为输入和标签
    input_text = f"Code: {code} Vulnerability: {vulnerability}"
    label_text = f"{vulnerability}"
    train_inputs.append(input_text)
    train_labels.append(label_text)
# 对训练数据进行编码
train_inputs_encoded = tokenizer(train_inputs, padding=True, truncation=True, return_tensors='pt', max_length=512)
train_labels_encoded = tokenizer(train_labels, padding=True, truncation=True, return_tensors='pt', max_length=512)
# 使用训练数据进行模型训练
model.train(
    input_ids=train_inputs_encoded.input_ids,
    attention_mask=train_inputs_encoded.attention_mask,
    labels=train_labels_encoded.input_ids
)

pythonCopy codefrom transformers import GPT2LMHeadModel, GPT2Tokenizer
# 加载训练好的模型和Tokenizer
model = GPT2LMHeadModel.from_pretrained('trained_model')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 输入待审计的代码
code = '...'
# 将代码编码为模型输入
input_text = f"Code: {code}"
input_encoded = tokenizer(input_text, padding=True, truncation=True, return_tensors='pt', max_length=512)
# 使用模型进行审计
output = model.generate(
    input_ids=input_encoded.input_ids,
    attention_mask=input_encoded.attention_mask,
    max_length=512,
    num_return_sequences=1,
    num_beams=5,
    no_repeat_ngram_size=2,
    early_stopping=True
)
# 解码模型输出
output_text = tokenizer.decode(output[0], skip_special_tokens=True)