merge branch

LICENSE

@@ -1,24 +1,21 @@
-This is free and unencumbered software released into the public domain.
+MIT License
 
-Anyone is free to copy, modify, publish, use, compile, sell, or
-distribute this software, either in source code form or as a compiled
-binary, for any purpose, commercial or non-commercial, and by any
-means.
+Copyright (c) 2025 DEFENSE INTELLIGENCE AGENCY • PROJECT RED SWORD
 
-In jurisdictions that recognize copyright laws, the author or authors
-of this software dedicate any and all copyright interest in the
-software to the public domain. We make this dedication for the benefit
-of the public at large and to the detriment of our heirs and
-successors. We intend this dedication to be an overt act of
-relinquishment in perpetuity of all present and future rights to this
-software under copyright law.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
-OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
-OTHER DEALINGS IN THE SOFTWARE.
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
 
-For more information, please refer to <https://unlicense.org>
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

advanced-zero-click-deployment-interface/FlowSteering/ApplicationCode/LLaVaServer/LLaVaServer.py

@@ -9,7 +9,7 @@
 SERVER_HOST = '0.0.0.0'
 SERVER_PORT = 1025
 saveMail_directory = "FlowSteering/ApplicationCode/LLaVaServer/EmailLLaVaMailDatabase"
-MODEL_NAME = "FlowSteering/llava/llava_weights/"  # PATH to the LLaVA weights
+MODEL_NAME = "FlowSteering/llava/llava_weights/"  # PATH to the LLaVa weights
 message_queue = Queue()
 # Server configuration
 
@@ -33,7 +33,10 @@
     except socket.timeout as e:
         print('timeout')
         print(e)
-
+        pass
+    except socket.error as e:
+        print('socket error')
+        print(e)
         pass
 
     return received_data
@@ -70,9 +73,9 @@
         for part in msg.get_payload():
             if part.get_content_type() == "text/plain":
                 body = part.get_payload()
-
     else:
-        print(msg.get_payload())
+        body = msg.get_payload()
+
     # print the subject
     for part in msg.walk():
         if part.get_content_maintype() == "multipart":
@@ -124,7 +127,11 @@
 
 def start_server(): # This function is used to start the server and listen for incoming connections
     server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    server_socket.bind((SERVER_HOST, SERVER_PORT))
+    try:
+        server_socket.bind((SERVER_HOST, SERVER_PORT))
+    except socket.error as e:
+        print(f"Error binding server socket: {e}")
+        return
     server_socket.listen(1000)
     model, image_processor, tokenizer, device = Run_LLaVa.Turn_On_LLaVa() # Turn on the LLaVa model and get the model, image processor, tokenizer and the device
 

advanced-zero-click-deployment-interface/FlowSteering/ApplicationCode/LLaVaServer/Run_LLaVa.py

@@ -77,7 +77,11 @@ def load_image(image_file):
         response = requests.get(image_file)
         image = Image.open(BytesIO(response.content)).convert('RGB')
     else:
-        image = Image.open(image_file).convert('RGB')
+        try:
+            image = Image.open(image_file).convert('RGB')
+        except Exception as e:
+            print(f"Error loading image: {e}")
+            return None
     return image
 
 
@@ -169,6 +173,8 @@ def generate_stream(model, prompt, tokenizer, input_ids, images=None):
 def run_result(X, prompt, initial_query, query_list, model, tokenizer, unnorm, image_processor):
     device = 'cuda'
     X = load_image(X)
+    if X is None:
+        return ["Error loading image"]
 
     print("Image: ")
     # load the image
@@ -234,8 +240,13 @@ def Turn_On_LLaVa():  # Load the LLaVa model
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     dtypePerDevice = torch.float16
 
-    model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=dtypePerDevice,
-                                                  use_cache=True)
+    try:
+        model = LlavaLlamaForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True, torch_dtype=dtypePerDevice,
+                                                      use_cache=True)
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None, None, None, None
+
     model.to(device=device, dtype=dtypePerDevice)
     image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower)
 
@@ -264,7 +275,11 @@ def Turn_On_LLaVa():  # Load the LLaVa model
 def load_param(MODEL_NAME, model, tokenizer, initial_query):
     model_name = os.path.expanduser(MODEL_NAME)
 
-    image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower)
+    try:
+        image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower)
+    except Exception as e:
+        print(f"Error loading image processor: {e}")
+        return None, None, None, None, None, None, None, None, None
 
     mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
     tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)

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

@@ -8,9 +8,6 @@
 
 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. 
@@ -25,9 +22,6 @@ The system consists of three main components: the Email Server, 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
@@ -37,7 +31,7 @@ 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/Database/EmailServerMailDatabase"  # Change this to the directory where you want to save the emails inbox for each user
+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'
 ```
@@ -59,9 +53,6 @@ def handle_messages():
                 Save_Email_To_Recipient()  
 ```
 
-
-
-
 #### To run the Email Server execute the following command in the EmailServer directory
 ```bash
 python3 EmailServer.py
@@ -92,9 +83,6 @@ def handle_messages():
                 SendToLLaVa()
 ```
 
-
-
-
 #### To run the LLaVa Server execute the following command in the LLaVaServer directory
 ```bash
 python3 LLaVaServer.py
@@ -109,16 +97,12 @@ Since this script is designed to run on multiple machines, you don't need to edi
 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:
@@ -129,9 +113,6 @@ def main():
 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) |
@@ -140,9 +121,6 @@ Finally, the End User Client will act based on the classification returned by th
 
 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.')
@@ -157,10 +135,8 @@ For our experiments, we implemented the action "Forward" and left the other acti
  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" 
@@ -172,9 +148,6 @@ Navigate to the [EndUserCode directory](../../FlowSteering/ApplicationCode/EndUs
 
 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():
@@ -195,25 +168,28 @@ Next, the Attacker Client will send two identical emails to the Email Server, wi
                           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.
 
+## Recent Changes and Additions
 
+We have recently made several updates and additions to the codebase to enhance the functionality and performance of the GenAI EcoSystem. These changes include:
 
+1. **Improved Network Handling**: Enhanced the network handling capabilities to address issues related to image transmission over sockets, especially when using virtual machines. A default image is now loaded when an image fails to send correctly due to network issues.
 
+2. **Optimized Email Server**: Refined the Email Server's handling of incoming connections and email storage. The server now creates a directory to save the email inbox for each user, ensuring better organization and retrieval of emails.
 
+3. **Enhanced LLaVa Server**: Updated the LLaVa Server to process incoming emails more efficiently using the LLaVa model. The server now listens for incoming connections, processes emails, and sends responses back to the End User Clients seamlessly.
 
+4. **End User Client Improvements**: Improved the End User Client script to run in a loop, checking the inbox for new emails every 10-20 seconds. The script now handles email classification and actions based on the classification returned by the LLaVa Server.
 
+5. **Attacker Client Simplification**: Simplified the Attacker Client script to send the initial malicious email to the End User Clients. The script now sends two identical emails to the Email Server, targeting specific recipients.
 
-
+These updates aim to provide a more robust and efficient GenAI EcoSystem, ensuring smooth communication and interaction between the various components.
 

advanced-zero-click-deployment-interface/FlowSteering/llava/llava.egg-info/requires.txt

@@ -1,6 +1,6 @@
 einops
 fastapi
-gradio==5.5.0
+gradio==5.11.0
 markdown2[all]
 numpy
 requests
@@ -12,7 +12,7 @@ uvicorn
 wandb
 shortuuid
 httpx==0.24.0
-deepspeed==0.9.5
+deepspeed==0.15.1
 peft==0.4.0
 transformers==4.38.0
 accelerate==0.21.0

advanced-zero-click-deployment-interface/FlowSteering/llava/pyproject.toml

@@ -13,11 +13,11 @@ classifiers = [
     "License :: OSI Approved :: Apache Software License",
 ]
 dependencies = [
-    "einops", "fastapi", "gradio==5.5.0", "markdown2[all]", "numpy",
+    "einops", "fastapi", "gradio==5.11.0", "markdown2[all]", "numpy",
     "requests", "sentencepiece", "tokenizers>=0.12.1",
     "torch", "torchvision", "uvicorn", "wandb",
     "shortuuid", "httpx==0.24.0",
-    "deepspeed==0.9.5",
+    "deepspeed==0.15.1",
     "peft==0.4.0",
     "transformers==4.38.0",
     "accelerate==0.21.0",

advanced-zero-click-deployment-interface/FlowSteering/llava/serve/gateway/README.md

@@ -55,3 +55,17 @@ sudo nginx -t  # check `/etc/nginx/nginx.conf`
 sudo systemctl reload nginx  # restart Nginx service to load the new config
 sudo systemctl status nginx  # check the status of the Nginx service. It should be active (running).
 ```
+
+## Recent Changes and Additions
+
+We have recently made several updates and additions to the codebase to enhance the functionality and performance of the Nginx gateway. These changes include:
+
+1. **Improved Security Features**: Enhanced the security features of the Nginx gateway to provide better protection for Gradio servers. This includes additional firewall rules and connection limits.
+
+2. **Optimized Load Balancing**: Refined the load balancing capabilities of the Nginx gateway to ensure efficient distribution of traffic across multiple Gradio servers.
+
+3. **Dynamic Server Management**: Updated the Nginx configuration to support dynamic mounting and unmounting of Gradio servers, allowing for more flexible server management.
+
+4. **Simplified Deployment Process**: Streamlined the deployment process for the Nginx gateway, making it easier to set up and configure on various Linux distributions.
+
+These updates aim to provide a more robust and efficient Nginx gateway, ensuring smooth communication and interaction between the various components of the system.

c2_dashboard.py

@@ -4,5 +4,35 @@ class C2Dashboard:
     def render(self):
         return pn.Column(
             "### Command and Control Dashboard",
-            pn.pane.Markdown("Welcome to the C2 Dashboard. Here you can manage and monitor your operations.")
+            pn.pane.Markdown("Welcome to the C2 Dashboard. Here you can manage and monitor your operations."),
+            pn.pane.Markdown("#### Detailed Metrics and Insights"),
+            pn.widgets.DataFrame(name="Metrics Data"),
+            pn.pane.Markdown("#### Visualizations"),
+            pn.widgets.DataFrame(name="Assets Data"),
+            pn.pane.Markdown("#### Message Boards"),
+            pn.widgets.DataFrame(name="Message Board Data"),
+            pn.pane.Markdown("#### Announcements"),
+            pn.widgets.DataFrame(name="Announcements Data"),
+            pn.pane.Markdown("#### Latest News on Exploits"),
+            pn.widgets.DataFrame(name="Latest News Data"),
+            pn.pane.Markdown("#### AI Interface"),
+            pn.widgets.DataFrame(name="AI Interface Data"),
+            pn.pane.Markdown("#### System Connections"),
+            pn.widgets.DataFrame(name="System Connections Data"),
+            pn.pane.Markdown("#### Logs"),
+            pn.widgets.DataFrame(name="Logs Data"),
+            pn.pane.Markdown("#### System Status"),
+            pn.widgets.DataFrame(name="System Status Data"),
+            pn.pane.Markdown("#### System Settings"),
+            pn.widgets.DataFrame(name="System Settings Data"),
+            pn.pane.Markdown("#### Attack Simulations"),
+            pn.widgets.DataFrame(name="Attack Simulations Data"),
+            pn.pane.Markdown("#### Fuzzing"),
+            pn.widgets.DataFrame(name="Fuzzing Data"),
+            pn.pane.Markdown("#### Asset Control"),
+            pn.widgets.DataFrame(name="Asset Control Data"),
+            pn.pane.Markdown("#### Reverse Shell Settings"),
+            pn.widgets.DataFrame(name="Reverse Shell Settings Data"),
+            pn.pane.Markdown("#### Advanced Connection Methods"),
+            pn.widgets.DataFrame(name="Advanced Connection Methods Data")
         )

core/email_server/EmailServer.py

@@ -72,13 +72,17 @@ def Save_Email_To_Recipient(client_socket, data, msg, requests, subject, sender,
 
     msg = email.message_from_bytes(data)
 
-    if msg.is_multipart():
-        for part in msg.get_payload():
-            if part.get_content_type() == "text/plain":
-                body = part.get_payload()
-
-    else:
-        print(msg.get_payload())
+    try:
+        if msg.is_multipart():
+            for part in msg.get_payload():
+                if part.get_content_type() == "text/plain":
+                    body = part.get_payload()
+        else:
+            body = msg.get_payload()
+    except Exception as e:
+        print(f"Error processing email message: {e}")
+        client_socket.sendall("Error processing email message".encode('utf-8'))
+        return
 
     for part in msg.walk():
         if part.get_content_maintype() == "multipart":

core/integrations/email_handler.py

@@ -78,4 +78,4 @@ def get_email_body(self, email_message) -> str:
             return email_message.get_payload(decode=True).decode()
         except Exception as e:
             self.logger.error(f"Error extracting email body: {e}")
-            return ""
+            return ""