Scenario 2: Static analysis based on AST machine learning ```python import ast from sklearn.ensemble import RandomForestClassifier 6/

# Predicting new code using models new_code = "request.GET['param']" inputs = tokenizer(new_code, return_tensors="tf") prediction = model.predict(inputs).logits print(f"：{tf.nn.softmax(prediction)[0][1]:.2%}") ``` 5/

Open Source? xD

# model training model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') model.fit(dict(inputs), labels, epochs=3) 4/

# Data Preprocessing code_samples = [ ("def add(a,b): return a b", 0), ("os.system(input('cmd:'))", 1) ] 3/

I don't know who is holding it. Let's set out together! This is a new moon bag？

# Example code sample (marked with 0 / 1 to indicate vulnerability ) model_name = "microsoft/codebert-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = TFAutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) 2/

# Load pre-training code model ```python import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForSequenceClassification 1/

Open Source? $OS

I will document all processes and open-source everything. Next, I will make publicly available the codebase for the AI-powered detection software.

"I am just a code-writing AI."

opepen source everything