Sinon - Modular Windows Burn-In Automation with Generative AI for Deception

Sinon is a modular tool for automatic burn-in of Windows-based deception hosts that aims to reduce the difficulty of orchestrating deception hosts at scale whilst enabling diversity and randomness through generative capabilities. It has been created as a proof-of-concept and is not intended for production deception environments. It would likely be better suited to having content pre-generated and built into a one-time script, as we wouldn't want to be storing secrets like OpenAI API keys on a decoy or deception host.

Features

Generative content including files, emails, and so on using OpenAI API (Configured for GPT-4o)
Randomness factor - select from list in config, or follow config completely
Temporal randomness - set delay to execution and delay between events including randomness factor

Sinon performs the following functions, as determined by a config file:

Install Applications: Automatically install applications from a predefined list using Chocolatey.
Browse Websites: Automatically open a list of websites to simulate user activity.
Change Preferences: Modify system preferences such as default browser, background images, screen resolutions, and system languages.
Add Start Menu Items: Add shortcuts to specified applications in the start menu.
Create and Modify Files: Generate and modify text files with the option to use OpenAI GPT-4 for content generation.
Send Emails: Send emails with the option to use OpenAI GPT-4 for content generation.
Download Decoy Files: Download files from specified URLs to simulate decoy file activity.
Manage Software: Install or uninstall software applications using predefined commands.
Perform System Updates: Execute system update commands.
Manage User Accounts: Create and manage user accounts with specified attributes.
Manage Network Settings: Configure Wi-Fi network connections using SSID and password.
Open Media Files: Open media files such as images, videos, and audio files.
Print Documents: Print specified text documents.
Create Scheduled Tasks: Schedule tasks to run specified commands at defined times.
Simulate User Interaction: Control the duration and delay of interactions with randomness.
Create Lures: Generate various types of lures to deceive intruders.
- Credential pairs
- SSH keys
- Website URLs
- Registry keys
- CSV documents
- API keys
- LNK files (shortcuts)
Monitor File System: Watch specified paths for file system events such as modifications and log these events.
Redis Connectivity: Send generated lure data to Redis server for utilisation in additional deception steps and platforms.

Usage

Clone the repository:

git clone https://github.com/yourusername/sinon.git
cd sinon

Configure the application:
- Modify the config.yaml file to suit your needs. See the Config Items section for details.

Build the application:

go build -o sinon
# building for windows on linux: GOOS=windows GOARCH=amd64 go build -o sinon.exe

Deploy the application to your target machine:
- This could be accomplished many ways, you may want to burn it in to an image, use SCCM/Intune etc.

Config Items

The config.yaml file contains all the configuration options for Sinon. Here is an example configuration file with explanations:

applications:
  options:
    - googlechrome
    - firefox
    - notepadplusplus
    - vlc
  selection_method: random

websites:
  options:
    - https://www.google.com
    - https://www.wikipedia.org
    - https://www.github.com
  selection_method: random

preferences:
  default_browser:
    options:
      - "C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"
      - "C:\\Program Files\\Mozilla Firefox\\firefox.exe"
    selection_method: random
  background_images:
    location: "C:\\Users\\user\\Pictures"
    type: http
    selection_method: random
    options:
      - https://example.com/background1.jpg
      - https://example.com/background2.jpg
  screen_resolutions:
    options:
      - "1920x1080"
      - "1366x768"
    selection_method: random
  languages:
    options:
      - en-US
      - es-ES
    selection_method: random

start_menu_items:
  options:
    - "C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"
    - "C:\\Program Files\\Mozilla Firefox\\firefox.exe"
  selection_method: random

file_operations:
  create_modify_files:
    - path: "C:\\Users\\user\\Documents\\example.txt"
      content: "This is an example text file."
      use_gpt: false
      gpt_prompt: ""

email_operations:
  google_account:
    email: "user@gmail.com"
    password: "password"
  microsoft_account:
    email: "user@outlook.com"
    password: "password"
  send_receive:
    - send_to: "recipient@example.com"
      subject: "Test Email"
      body: "This is a test email."
      use_gpt: true
      gpt_prompt: "Write a friendly email to a colleague."

software_management:
  options:
    - upgrade all
    - uninstall vlc
  selection_method: random

system_updates:
  method: install_all
  specific_updates:
    - KB123456
    - KB789012
  selection_method: random
  hide_updates:
    - KB654321
    - KB210987

user_accounts:
  - name: user1
    password: password1
    full_name: User One
    description: First user account
  - name: user2
    password: password2
    full_name: User Two
    description: Second user account

network_settings:
  - ssid: ExampleSSID
    password: examplepassword

system_logs:
  options:
    - Application
    - System
  selection_method: random

media_files:
  location: "C:\\Users\\user\\Videos"
  type: http
  selection_method: random
  options:
    - https://example.com/video1.mp4
    - https://example.com/video2.mp4

printing:
  options:
    - "C:\\Users\\user\\Documents\\print_me.txt"
  selection_method: random

scheduled_tasks:
  options:
    - name: Task1
      path: "C:\\Windows\\System32\\notepad.exe"
      schedule: "daily"
      start_time: "14:00"
    - name: Task2
      path: "C:\\Windows\\System32\\calc.exe"
      schedule: "weekly"
      start_time: "10:00"
  selection_method: random

decoy_files:
  sets:
    - location:
        - "https://example.com/decoy1.txt"
        - "https://example.com/decoy2.txt"
      type: http
      target_directory:
        - "C:\\Users\\user\\Documents"
      selection_method: random

lures:
  - name: CredentialLure
    type: credential_pair
    location: "C:\\Users\\user\\Desktop\\credential.txt"
    generation_params:
      length: 12
    generative_type: golang
    openai_prompt: ""
  - name: SSLLure
    type: ssh_key
    location: "C:\\Users\\user\\Desktop\\id_rsa"
    generation_params: {}
    generative_type: golang
    openai_prompt: ""
  - name: URLLure
    type: website_url
    location: "C:\\Users\\user\\Desktop\\phishing_link.url"
    generation_params:
      base_url: "https://malicious.example.com"
    generative_type: golang
    openai_prompt: ""
  - name: RegistryLure
    type: registry_key
    location: "HKEY_CURRENT_USER\\Software\\ExampleKey"
    generation_params:
      registry_key_type: "REG_SZ"
      registry_key_value: "ExampleValue"
    generative_type: golang
    openai_prompt: ""
  - name: CSVLure
    type: csv
    location: "C:\\Users\\user\\Desktop\\financial_records.csv"
    generation_params:
      document_content: "Date,Amount,Description\n2024-01-01,1000,Salary"
    generative_type: golang
    openai_prompt: ""
  - name: APIKeyLure
    type: api_key
    location: "C:\\Users\\user\\Desktop\\api_key.txt"
    generation_params:
      api_key_format: "uuid"
    generative_type: golang
    openai_prompt: ""
  - name: LNKLure
    type: lnk
    location: "C:\\Users\\user\\Desktop\\shortcut.lnk"
    generation_params:
      target_path: "C:\\Windows\\System32\\notepad.exe"
    generative_type: golang
    openai_prompt: ""

general:
  redis:
    ip: "127.0.0.1"
    port: 6379
  log_file: "C:\\Users\\user\\sinon.log"
  openai_api_key: "your_openai_api_key"
  interaction_duration: 60
  action_delay: 5
  randomness_factor: 2

Deploying Windows Deception Hosts

Sinon is designed to automate the setup of deception hosts by performing a variety of actions that simulate real user activity. The goal is to create a realistic environment that can deceive potential intruders. The modular and configurable nature of Sinon allows for easy adjustments and randomization, making each deployment unique.

Steps to Deploy

Prepare the Windows environment: Ensure that the target Windows machine is ready and accessible.
Configure Sinon: Edit the config.yaml file to define the desired behaviors and settings.
Run Sinon: Execute the compiled Sinon binary to start the automation process.
Monitor and manage: Keep an eye on the deployed deception host and make necessary adjustments to the configuration as needed.

Note: Since Sinon is a proof-of-concept, it is recommended to use pre-generated content and avoid storing sensitive information like API keys on deception hosts in production environments.

referefref / sinon