import torch
import numpy as np
from neural.shape_propagation.shape_propagator import ShapePropagator

def hacky_mode(propagator, model_data):
  """Analyze network for security vulnerabilities and breaches."""
  # Gradient Leakage (inspired by HackingNeuralNetworks)
  def check_gradient_leakage(model):
      if torch.cuda.is_available():
          for param in model.parameters():
              grad = param.grad
              if grad is not None and torch.any(torch.isnan(grad)):
                  print(f"Gradient leakage detected: NaN values in {param}")
  
  # Adversarial Input Simulation
  def simulate_adversarial_input(input_shape):
      noise = torch.randn(*input_shape) * 0.1
      adversarial_input = torch.randn(*input_shape) + noise
      trace = propagator.propagate(adversarial_input, model_data['layers'][0], model_data['framework'])
      print(f"Adversarial input effect: {trace['execution_time']} vs normal {propagator.execution_trace[-1]['execution_time']}")

  click.echo("Analyzing gradient leakage...")
  check_gradient_leakage(model_data)  # Assume model is built
  click.echo("Simulating adversarial input...")
  simulate_adversarial_input(model_data['input']['shape'])