Merge branch 'Your-Momma-Beeotch' into fix-archive-gui

Author: ProjectZeroDays

.github/workflows/deploy_huggingface.yml

@@ -0,0 +1,30 @@
+name: Deploy to Huggingface
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.x'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+
+    - name: Deploy to Huggingface
+      env:
+        HUGGINGFACE_API_KEY: ${{ secrets.HUGGINGFACE_API_KEY }}
+      run: |
+        bash scripts/deploy_huggingface.sh

Dockerfile

@@ -22,4 +22,4 @@ ENV OPENAI_API_KEY=${OPENAI_API_KEY}
 ENV HUGGINGFACE_API_KEY=${HUGGINGFACE_API_KEY}
 
 # Command to start the Gradio app
-CMD ["python", "src/frontend/archive_gui.py"]
+CMD ["python", "frontend/archive_gui.py"]

README.md

@@ -333,3 +333,19 @@ We have recently added several new dashboards and functionalities to the Project
 24. **Vulnerability Scanner**: Added a new dashboard for the vulnerability scanner, providing comprehensive scanning and reporting of vulnerabilities.
 25. **Wireless Exploitation**: Enhanced the wireless exploitation dashboard with new tools and techniques for exploiting wireless vulnerabilities.
 26. **Zero Day Exploits**: Added a new dashboard for managing zero-day exploits, including identification and deployment of exploits.
+
+### Huggingface Deployment Automation
+
+To automate the deployment process for Huggingface, follow these steps:
+
+1. **Create a deployment script**: Add a script named `scripts/deploy_huggingface.sh` that includes the necessary commands to deploy your application to Huggingface. Ensure the script includes commands to authenticate with Huggingface, upload your model or dataset, and any other necessary steps.
+
+2. **Define environment variables**: Define the necessary environment variables for Huggingface deployment, such as `HUGGINGFACE_API_KEY`. You can set these environment variables in your deployment script or in your GitHub Actions workflow file.
+
+3. **Integrate with GitHub Actions**: Integrate the Huggingface deployment script with GitHub Actions by creating a workflow file named `.github/workflows/deploy_huggingface.yml`. Define the necessary jobs and steps in the workflow file to run the deployment script. Ensure that the workflow file includes steps to set up the environment, install dependencies, and run the deployment script.
+
+4. **Install dependencies**: Ensure that all dependencies required for the Huggingface deployment script are installed. You can specify the dependencies in a `requirements.txt` file or directly in the deployment script.
+
+5. **Create and manage API keys**: Learn about creating and managing API keys for Huggingface by referring to the Huggingface documentation. Store the API keys securely using GitHub Secrets or other secure methods. Ensure that the API keys are not exposed in your code or version control system.
+
+By following these steps, you can automate the deployment process for Huggingface and ensure a smooth and efficient deployment of your application.

ai/ai_simulations.py

@@ -17,8 +17,6 @@ def simulate_attack(self):
             return
 
         try:
-            if not self.scenarios:
-                raise IndexError("No scenarios available.")
             scenario = random.choice(self.scenarios)
             print(f"[SIMULATION] Executing simulated attack: {scenario}")
 

app.py

@@ -71,20 +71,19 @@
 
 
 async def random_url(_):
-    retries = 3
-    for _ in range(retries):
-        try:
-            pet = random.choice(["cat", "dog"])
-            api_url = f"https://api.the{pet}api.com/v1/images/search"
-            async with aiohttp.ClientSession() as session:
-                async with session.get(api_url) as resp:
-                    resp.raise_for_status()
-                    return (await resp.json())[0]["url"]
-        except aiohttp.ClientError as e:
-            logging.error(f"API request failed: {e}")
-        except Exception as e:
-            logging.error(f"Unexpected error: {e}")
-    return None
+    try:
+        pet = random.choice(["cat", "dog"])
+        api_url = f"https://api.the{pet}api.com/v1/images/search"
+        async with aiohttp.ClientSession() as session:
+            async with session.get(api_url) as resp:
+                resp.raise_for_status()
+                return (await resp.json())[0]["url"]
+    except aiohttp.ClientError as e:
+        logging.error(f"API request failed: {e}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None
 
 
 @pn.cache
@@ -270,7 +269,7 @@ async def process_inputs(class_names: List[str], image_url: str):
     macos_control = MacOSControl()
     linux_control = LinuxControl()
     android_control = AndroidControl()
-    ios_control = iOSControl()
+    ios_control = IOSControl()
     advanced_device_control = AdvancedDeviceControl()
     code_parser = CodeParser("sample_code")
     pipeline_manager = PipelineManager()

backend/ai_chat.py

@@ -21,6 +21,9 @@ def openai_chat(self, prompt):
             openai.api_key = self.openai_key
             response = openai.Completion.create(engine="text-davinci-003", prompt=prompt, max_tokens=100)
             return response.choices[0].text.strip()
+        except openai.error.AuthenticationError as e:
+            logging.error(f"Authentication error during OpenAI chat: {e}")
+            return "Authentication error"
         except Exception as e:
             logging.error(f"Error during OpenAI chat: {e}")
             return ""

chatbot/app.py

@@ -396,6 +396,22 @@ def receive_message_from_kafka(consumer):
     except Exception as e:
         logging.error(f"Error receiving message from Kafka: {e}")
 
+# Integrate email spoofing techniques
+def email_spoofing(target_email, spoofed_email, subject, message):
+    try:
+        return advanced_social_engineering.email_spoofing_attack(target_email, spoofed_email, subject, message)
+    except Exception as e:
+        logging.error(f"Error during email spoofing: {e}")
+        return "Email spoofing failed."
+
+# Integrate SMS spoofing techniques
+def sms_spoofing(target_number, spoofed_number, message):
+    try:
+        return advanced_social_engineering.sms_spoofing_attack(target_number, spoofed_number, message)
+    except Exception as e:
+        logging.error(f"Error during SMS spoofing: {e}")
+        return "SMS spoofing failed."
+
 if __name__ == "__main__":
     channel = setup_message_queue()
     if channel:

infra/Dockerfile

@@ -1,4 +1,3 @@
-
 # Base image
 FROM python:3.9-slim
 
@@ -9,10 +8,11 @@ WORKDIR /app
 COPY . .
 
 # Install dependencies
+RUN apt-get update && apt-get install -y libgl1-mesa-glx
 RUN pip install -r requirements.txt
 
 # Expose application port
 EXPOSE 5000
 
 # Run the application
-CMD ["python", "src/frontend/gui_chat_interface.py"]
+CMD ["python", "frontend/archive_gui.py"]

infra/test_deployment.sh

@@ -1,4 +1,3 @@
-
 #!/bin/bash
 
 echo "Starting Deployment Testing..."
@@ -35,4 +34,13 @@ fi
 echo "Checking Kubernetes Pods..."
 kubectl get pods
 
+# Test Huggingface Deployment
+echo "Testing Huggingface Deployment..."
+bash scripts/deploy_huggingface.sh
+if [ $? -eq 0 ]; then
+    echo "Huggingface Deployment Tested Successfully."
+else
+    echo "Huggingface Deployment Test Failed!" && exit 1
+fi
+
 echo "Deployment Testing Completed."

modules/advanced_social_engineering.py

@@ -2,7 +2,7 @@
 
 class AdvancedSocialEngineering:
     def __init__(self):
-        self.attack_types = ["phishing", "spear_phishing", "whaling"]
+        self.attack_types = ["phishing", "spear_phishing", "whaling", "email_spoofing", "sms_spoofing"]
 
     def execute_attack(self, attack_type, target):
         if attack_type not in self.attack_types:
@@ -15,6 +15,10 @@ def execute_attack(self, attack_type, target):
             return self.spear_phishing_attack(target)
         elif attack_type == "whaling":
             return self.whaling_attack(target)
+        elif attack_type == "email_spoofing":
+            return self.email_spoofing_attack(target)
+        elif attack_type == "sms_spoofing":
+            return self.sms_spoofing_attack(target)
 
     def phishing_attack(self, target):
         logging.info(f"Executing phishing attack on target: {target}")
@@ -31,5 +35,15 @@ def whaling_attack(self, target):
         # Placeholder for whaling attack logic
         return f"Whaling attack executed on {target}"
 
+    def email_spoofing_attack(self, target_email, spoofed_email, subject, message):
+        logging.info(f"Executing email spoofing attack on target: {target_email}")
+        # Placeholder for email spoofing attack logic
+        return f"Email spoofing attack executed on {target_email} with spoofed email {spoofed_email}, subject {subject}, and message {message}"
+
+    def sms_spoofing_attack(self, target_number, spoofed_number, message):
+        logging.info(f"Executing SMS spoofing attack on target: {target_number}")
+        # Placeholder for SMS spoofing attack logic
+        return f"SMS spoofing attack executed on {target_number} with spoofed number {spoofed_number} and message {message}"
+
     def render(self):
-        return "Advanced Social Engineering Module: Ready to execute phishing, spear phishing, and whaling attacks."
+        return "Advanced Social Engineering Module: Ready to execute phishing, spear phishing, whaling, email spoofing, and SMS spoofing attacks."