infosec documentation

Former-commit-id: cc0173da5c

docs/features/info_sec.md

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+# Secure Communication Protocols
+
+## Overview
+
+The Swarms Multi-Agent Framework prioritizes the security and integrity of data, especially personal and sensitive information. Our Secure Communication Protocols ensure that all communications between agents are encrypted, authenticated, and resistant to tampering or unauthorized access.
+
+## Features
+
+### 1. End-to-End Encryption
+
+- All inter-agent communications are encrypted using state-of-the-art cryptographic algorithms.
+- This ensures that data remains confidential and can only be read by the intended recipient agent.
+
+### 2. Authentication
+
+- Before initiating communication, agents authenticate each other using digital certificates.
+- This prevents impersonation attacks and ensures that agents are communicating with legitimate counterparts.
+
+### 3. Forward Secrecy
+
+- Key exchange mechanisms employ forward secrecy, meaning that even if a malicious actor gains access to an encryption key, they cannot decrypt past communications.
+  
+### 4. Data Integrity
+
+- Cryptographic hashes ensure that the data has not been altered in transit.
+- Any discrepancies in data integrity result in the communication being rejected.
+
+### 5. Zero-Knowledge Protocols
+
+- When handling especially sensitive data, agents use zero-knowledge proofs to validate information without revealing the actual data.
+  
+### 6. Periodic Key Rotation
+
+- To mitigate the risk of long-term key exposure, encryption keys are periodically rotated.
+- Old keys are securely discarded, ensuring that even if they are compromised, they cannot be used to decrypt communications.
+
+## Best Practices for Handling Personal and Sensitive Information
+
+1. **Data Minimization**: Agents should only request and process the minimum amount of personal data necessary for the task.
+2. **Anonymization**: Whenever possible, agents should anonymize personal data, stripping away identifying details.
+3. **Data Retention Policies**: Personal data should be retained only for the period necessary to complete the task, after which it should be securely deleted.
+4. **Access Controls**: Ensure that only authorized agents have access to personal and sensitive information. Implement strict access control mechanisms.
+5. **Regular Audits**: Conduct regular security audits to ensure compliance with privacy regulations and to detect any potential vulnerabilities.
+6. **Training**: All agents should be regularly updated and trained on the latest security protocols and best practices for handling sensitive data.
+
+## Conclusion
+
+Secure communication is paramount in the Swarms Multi-Agent Framework, especially when dealing with personal and sensitive information. Adhering to these protocols and best practices ensures the safety, privacy, and trust of all stakeholders involved.

swarmsui.py

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+# Import required libraries
+from gradio import Interface, Textbox, HTML, Blocks, Row, Column
+import threading
+import os
+import glob
+import base64
+from langchain.llms import OpenAIChat  # Replace with your actual class
+from swarms.agents import OmniModalAgent  # Replace with your actual class
+
+# Function to convert image to base64
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode()
+
+# Function to get the most recently created image in the directory
+def get_latest_image():
+    list_of_files = glob.glob('./*.png')
+    if not list_of_files:
+        return None
+    latest_file = max(list_of_files, key=os.path.getctime)
+    return latest_file
+
+# Initialize your OmniModalAgent
+llm = OpenAIChat(model_name="gpt-4")
+agent = OmniModalAgent(llm)
+
+# Global variable to store chat history
+chat_history = []
+
+# Function to update chat
+def update_chat(user_input):
+    global chat_history
+    chat_history.append({"type": "user", "content": user_input})
+        
+    agent_response = agent.run(user_input)
+    if not isinstance(agent_response, dict):
+        agent_response = {"type": "text", "content": str(agent_response)}
+    chat_history.append(agent_response)
+
+    latest_image = get_latest_image()
+    if latest_image:
+        chat_history.append({"type": "image", "content": latest_image})
+
+    return render_chat(chat_history)
+
+# Function to render chat as HTML
+def render_chat(chat_history):
+    chat_str = "<div style='max-height:400px;overflow-y:scroll;'>"
+    for message in chat_history:
+        if message['type'] == 'user':
+            chat_str += f"<p><strong>User:</strong> {message['content']}</p>"
+        elif message['type'] == 'text':
+            chat_str += f"<p><strong>Agent:</strong> {message['content']}</p>"
+        elif message['type'] == 'image':
+            img_path = os.path.join(".", message['content'])
+            base64_img = image_to_base64(img_path)
+            chat_str += f"<p><strong>Agent:</strong> <img src='data:image/png;base64,{base64_img}' alt='image' width='200'/></p>"
+    chat_str += "</div>"
+    return chat_str
+
+# Define layout using Blocks
+with Blocks() as app_blocks:
+    with Row():
+        with Column():
+            chat_output = HTML(label="Chat History")
+    with Row():
+        with Column():
+            user_input = Textbox(label="Your Message", type="text")
+
+# Define Gradio interface
+iface = Interface(
+    fn=update_chat, 
+    inputs=user_input,
+    outputs=chat_output,
+    live=False,
+    layout=app_blocks
+)
+
+# Function to update the chat display
+def update_display():
+    global chat_history
+    while True:
+        iface.update(render_chat(chat_history))
+
+# Run the update_display function in a separate thread
+threading.Thread(target=update_display).start()
+
+# Run Gradio interface
+iface.launch()