modules/advanced-zero-click-deployment-interface/FlowSteering/ApplicationCode/README.md

# Running the GenAI EcoSystem

<p align="center">
  <img src="../../Assets/GenAIEcosystem.png" alt="GenAI" style="width:700px;height:400px;">
</p>

## Overview

In this directory, you will find the code for the GenAI EcoSystem. The GenAI EcoSystem consists of a collection of scripts designed to simulate an email system with multiple users and dedicated servers.




The system consists of three main components: the Email Server, the LLaVa Server, and the End User Clients. 
* The Email Server is responsible for sending and receiving emails from the End User Clients.
* The LLaVa Server is the GenAI service responsible for handling the emails that were sent to the End User Clients. 
* The End User Clients are the users of the system. They send and receive emails from the Email Server and interact with the LLaVa Server.
* The Attacker Client is a script that simulates an attacker that sends emails to the End User Clients.

## Prerequisites to run the GenAI EcoSystem

1. Access to a  PC\Server with a GPU that can run LLaVa,and can communicate through Socket with the End User Clients.
2. Access to a PC\Server that can run the Email Server and can communicate through Socket with the End User Clients.
3. Virtual machines or physical machines that can run the End User Clients and can communicate through Socket with the Email Server and the LLaVa Server.

In our experiments, we utilized a single machine to run both the Email Server and the LLaVa Server. This machine was equipped with a single NVIDIA Quadro RTX 6000 24GB GPU. Additionally, we employed seven virtual machines to run the End User Clients.




## Running the GenAI EcoSystem

### 1. Run the Email Server
navigate to the [EmailServer directory](../../FlowSteering/ApplicationCode/EmailServer/) and edit the [EmailServer.py](../../FlowSteering/ApplicationCode/EmailServer/EmailServer.py) 
file to set the server configuration. 

```python
SERVER_HOST = '0.0.0.0' # Change this to the IP address of the machine where the Email Server will run
SERVER_PORT = 1234 # Change this to the port where the Email Server will listen
saveMail_directory = "FlowSteering/ApplicationCode/EmailServer/EmailServerMailDatabase"  # Change this to the directory where you want to save the emails inbox for each user
message_queue = Queue()
default_image = 'FlowSteering/assets/PerturbatedImages/DjiPerturbClassForward.png'
```

The Email server will listen for incoming connections from the End User Clients and create a directory to save the email inbox for each user. 

We encountered some network issues when sending images through the socket, particularly when using virtual machines. Therefore, we implemented a solution by adding a default image to be loaded when an image fails to send correctly due to network issues.

#### The Email server can handle two requests from the End User Clients:
* **CHECK_INBOX**: This command is used to check the inbox of the user and send the email to the client
* **SEND_EMAIL**: This command is used to save a new email to the recipient's inbox
```python
def handle_messages():
     if Command == "CHECK_INBOX":
                print("Checking Inbox")
                Check_Inbox()  
     elif Command == "SEND_EMAIL":  
                print("Sending Email")
                Save_Email_To_Recipient()  
```




#### To run the Email Server execute the following command in the EmailServer directory
```bash
python3 EmailServer.py
```

### 2. Run the LLaVa Server

navigate to the [LLaVaServer directory](../../FlowSteering/ApplicationCode/LLaVaServer) and edit the [LLaVaServer.py](../../FlowSteering/ApplicationCode/LLaVaServer/LLaVaServer.py)
file to set the server configuration. 

```python
SERVER_HOST = '0.0.0.0' # Change this to the IP address of the machine where the LLaVa Server will run
SERVER_PORT = 1025 # Change this to the port where the LLaVa Server will listen
saveMail_directory = "FlowSteering/ApplicationCode/LLaVaServer/EmailLLaVaMailDatabase" # Change this to the directory where you want to save the images sent to the LLaVa Server
MODEL_NAME = "FlowSteering/llava/llava_weights/"  # PATH to the LLaVA weights
message_queue = Queue()
```

The LLaVa server will listen for incoming connections from the End User Clients, process the incoming emails using the LLaVa model, and then send the response back to the End User Clients.

#### The LLaVa server can handle one request from the End User Clients:
* **CHECK_EMAIL**: This command is used to request the LLaVa server to send the email to the LLaVa model for classification.

```python
def handle_messages():
     if Command == "CHECK_EMAIL": 
                print("Sending the Email to LLaVa model for classification")
                SendToLLaVa()
```




#### To run the LLaVa Server execute the following command in the LLaVaServer directory
```bash
python3 LLaVaServer.py
```

### 3. Run the End User Clients

Navigate to the [EndUserCode directory](../../FlowSteering/ApplicationCode/EndUserCode) 
Since this script is designed to run on multiple machines, you don't need to edit the  [EndUserClient.py](../../FlowSteering/ApplicationCode/EndUserCode/EndUserClient.py) file.
 However, to run the End User Clients, you need to provide the function with some parameters. One of these parameters is a CSV file containing emails that each End User Client has received previously.

You can find an example of the CSV file named: [EndUserBaseEmails.csv](../../FlowSteering/ApplicationCode/EndUserCode/EndUserClientBaseEmails/EndUserBaseEmails.csv).
The function responsible for reading this CSV file is located in the [EndUserClient.py](../../FlowSteering/ApplicationCode/EndUserCode/EndUserClient.py) file under the respective function.


```python
def read_emails_from_file():
```



The script for each End User Client runs in a loop, sending a request to the Email Server to check the inbox for new emails every 10-20 seconds.


```python
def main():
    while True:
        time.sleep(random.randint(10, 20))
        print('Checking the inbox for new emails')
        check_email_inbox()
``` 
If there is a new email in the inbox, the Email server will send the email to the End User Client, and a pop-up window will appear with the email content.
Next the End User Client will send the email to the LLaVa Server for classification, and the LLaVa Server will send the classification back to the End User Client.




| Pop-up Window                               | Queries sent to LLaVa                               |
|---------------------------------------------|-----------------------------------------------------|
| ![Image 1 Description](../../Assets/DJISpam.png) | ![Image 2 Description](../../Assets/LLaVaQuery.png) |

Finally, the End User Client will act based on the classification returned by the LLaVa Server. 

For our experiments, we implemented the action "Forward" and left the other actions as placeholders.




```python
 if Classification == 'reply':
        print('Manual action is required for replying to this email, so it will be transferred to the Manual Folder.')
        pass
 elif Classification == 'forward':
        print('Starting to forward the emails to the correspondents')
        Command = "SEND_EMAIL"
        EmailAddresses = list(set(EmailAddresses))
        for Email in EmailAddresses:
            recipient = Email
            response = send_Email(recipient)           
 elif Classification == 'spam':
        print('Moving the email to the Spam Folder')
        pass
    
```


#### To run the End User Client execute the following command in the EndUserCode directory and replace the configurations of the server and the user with your own configurations
```bash
python3 EndUserClient.py --SERVER_EMAIL_HOST 111.88.88.33 --SERVER_EMAIL_PORT 1234 --SERVER_LLAVA_HOST 111.55.55.33 --SERVER_LLAVA_PORT 1025 --MYEMAIL Person1@example.com --saveMail_directory "FlowSteering/ApplicationCode/EndUserCode/EndUserPersonalEmailDir" --BaseEmails_directory "FlowSteering/ApplicationCode/EndUserCode/EndUserClientBaseEmails/EndUserBaseEmails.csv" --CycleNewEmails True --default_image "FlowSteering/assets/PerturbatedImages/DjiPerturbClassForward.png" 
```

### 4. Run the Attacker Client

Navigate to the [EndUserCode directory](../../FlowSteering/ApplicationCode/EndUserCode) and edit the [AttackerClient.py](../../FlowSteering/ApplicationCode/EndUserCode/AttackerClient.py) file to send the first malicious email to the End User Clients.

This code is a simplified version of the End User Client, used solely to send the initial malicious email to the End User Clients, as they are not composing new emails.




Configure the following variables to send the email:
``` python
def main():
    ...
    subject = "Black Friday Deal!"
    attachment_path = "../PerturbatedImages/DjiPerturbClassForward.png" # path to the attachment of the perturbated image
    body = 'Happy Cyber Monday Cornell ! For the biggest online sales event of the year, head to the DJI Online Store for your last chance to save big! Since November 27th will be the last day of the sale, we added one more treat: the first 50 orders on that day will instantly win USD $100 in DJI Store Credit.'
    recipient1 = 'Person1@example.com'
    recipient2 = 'Person6@example.com'
```

Next, the Attacker Client will send two identical emails to the Email Server, with recipient1 and recipient2 as the recipients.

```python
    response = send_Email(Command, sender, recipient1, subject, body, attachment_path, SERVER_EMAIL_HOST,
                          SERVER_EMAIL_PORT)
    response = send_Email(Command, sender, recipient2, subject, body, attachment_path, SERVER_EMAIL_HOST,
                          SERVER_EMAIL_PORT)
 ```




#### To run the Attacker Client execute the following command in the EndUserCode directory and replace the configurations of the server and the user with your own configurations
```bash
python3 AttackerClient.py --SERVER_EMAIL_HOST 111.88.88.33 --SERVER_EMAIL_PORT 1234 --SERVER_LLAVA_HOST 111.55.55.33 --SERVER_LLAVA_PORT 1025 --MYEMAIL Attacker@example.com 
```


## Conclusion

In our experiments, we developed a basic GenAI email application consisting of several components. You are welcome to modify any part of the system and tailor it to your own requirements and preferences.