AI Penetration Tester (Code Behind It)

A developer shared code for an AI penetration tester that uses a neural network to prioritize network targets for vulnerability scanning. The tool combines Scapy for network discovery, requests for web app checks, and a PyTorch model to predict likely vulnerable hosts.

| python | | | ai pen tester.py | | | import torch | | | import torch.nn as nn | | | import scapy.all as scapy | | | import requests | | | import nmap | | | Define the AI model architecture | | | class PenTestModel nn.Module : | | | def init self : | | | super PenTestModel, self . init | | | self.fc1 = nn.Linear 128, 128 input layer 128 - hidden layer 128 | | | self.fc2 = nn.Linear 128, 128 hidden layer 128 - hidden layer 128 | | | self.fc3 = nn.Linear 128, 2 hidden layer 128 - output layer 2 | | | def forward self, x : | | | x = torch.relu self.fc1 x | | | x = torch.relu self.fc2 x | | | x = self.fc3 x | | | return x | | | Define the penetration testing functions | | | class PenTester: | | | def init self, model : | | | self.model = model | | | def scan network self, ip range : | | | Use scapy to perform a basic network scan | | | arp request = scapy.ARP pdst=ip range | | | broadcast = scapy.Ether dst="ff:ff:ff:ff:ff:ff" | | | arp request broadcast = broadcast/arp request | | | answered list = scapy.srp arp request broadcast, timeout=1, verbose=False 0 | | | Create a list of IP addresses to scan | | | ip addresses = answered list i 1 .psrc for i in range len answered list | | | return ip addresses | | | def scan web app self, ip address : | | | Use requests to perform a basic web application scan | | | url = f"http://{ip address}" | | | response = requests.get url | | | Check for common web application vulnerabilities | | | if response.status code == 200: | | | Check for SQL injection vulnerabilities | | | sql injection url = f"{url}/?id=1' OR '1' = '1" | | | sql injection response = requests.get sql injection url | | | if sql injection response.status code == 200: | | | return True | | | Check for cross-site scripting XSS vulnerabilities | | | xss url = f"{url}/?name=<script alert 'XSS' </script " | | | xss response = requests.get xss url | | | if xss response.status code == 200: | | | return True | | | return False | | | def run pen test self, ip range : | | | Scan the network and identify potential targets | | | ip addresses = self.scan network ip range | | | Use the AI model to predict which targets are most likely to be vulnerable | | | predictions = | | | for ip address in ip addresses: | | | Create a feature vector for the target | | | features = 1, 0, 1, 0 placeholder features | | | features = torch.tensor features, dtype=torch.float32 | | | Run the feature vector through the AI model | | | output = self.model features | | | prediction = torch.argmax output | | | Add the prediction to the list | | | predictions.append ip address, prediction | | | Sort the predictions by confidence | | | predictions.sort key=lambda x: x 1 , reverse=True | | | Run the penetration test on the top N targets | | | for ip address, in predictions :5 : | | | if self.scan web app ip address : | | | print f"Vulnerability found on {ip address} " | | | Create an instance of the AI model and the penetration tester | | | model = PenTestModel | | | pen tester = PenTester model | | | Run the penetration test | | | pen tester.run pen test "192.168.1.0/24" |