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👋_Welcome.py
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👋_Welcome.py
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"""
Copyright (C) 2024, Matthew Adams
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
A copy of the licence is provided with this program. If you are unable
to view it, please see https://www.gnu.org/licenses/
"""
import streamlit as st
import requests
import os
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# ------------------ Streamlit UI Configuration ------------------ #
st.set_page_config(
page_title="AttackGen",
page_icon="👾",
)
# ------------------ Sidebar ------------------ #
with st.sidebar:
st.sidebar.markdown("### <span style='color: #1DB954;'>Setup</span>", unsafe_allow_html=True)
# Add model selection input field to the sidebar
model_provider = st.selectbox(
"Select your preferred model provider:",
["OpenAI API", "Azure OpenAI Service", "Google AI API", "Mistral API", "Ollama"],
key="model_provider",
help="Select the model provider you would like to use. This will determine the models available for selection.",
)
# Save the selected model provider to the session state
st.session_state["chosen_model_provider"] = model_provider
if model_provider == "OpenAI API":
# Check if OpenAI API key is in environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
# Add OpenAI API key input field to the sidebar if not in environment
st.session_state["openai_api_key"] = st.text_input(
"Enter your OpenAI API key:",
type="password",
help="You can find your OpenAI API key on the [OpenAI dashboard](https://platform.openai.com/account/api-keys).",
)
else:
st.session_state["openai_api_key"] = openai_api_key
st.success("API key loaded from .env")
# Add model selection input field to the sidebar
model_name = st.selectbox(
"Select the model you would like to use:",
["gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "o1-preview", "o1-mini"],
key="selected_model",
help="GPT-4o and GPT-4o mini are OpenAI's latest models and are recommended. o1-preview and o1-mini are new models available for testing.",
)
st.session_state["model_name"] = model_name
if model_provider == "Azure OpenAI Service":
# Check if Azure OpenAI API key is in environment variables
azure_api_key = os.getenv("AZURE_OPENAI_API_KEY")
if not azure_api_key:
# Add Azure OpenAI API key input field to the sidebar if not in environment
st.session_state["AZURE_OPENAI_API_KEY"] = st.text_input(
"Azure OpenAI API key:",
type="password",
help="You can find your Azure OpenAI API key on the [Azure portal](https://portal.azure.com/).",
)
else:
st.session_state["AZURE_OPENAI_API_KEY"] = azure_api_key
st.success("API key loaded from .env")
# Check if Azure OpenAI endpoint is in environment variables
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
if not azure_endpoint:
# Add Azure OpenAI endpoint input field to the sidebar if not in environment
st.session_state["AZURE_OPENAI_ENDPOINT"] = st.text_input(
"Azure OpenAI endpoint:",
help="Example endpoint: https://YOUR_RESOURCE_NAME.openai.azure.com/",
)
else:
st.session_state["AZURE_OPENAI_ENDPOINT"] = azure_endpoint
st.success("Endpoint loaded from .env")
# Add Azure OpenAI deployment name input field to the sidebar
azure_deployment = os.getenv("AZURE_DEPLOYMENT")
if not azure_deployment:
st.session_state["AZURE_DEPLOYMENT"] = st.text_input(
"Deployment name:",
help="This is the name of your Azure OpenAI deployment.",
)
else:
st.session_state["AZURE_DEPLOYMENT"] = azure_deployment
st.success("Deployment name loaded from .env")
# Add API version dropdown selector to the sidebar
st.session_state["openai_api_version"] = st.selectbox("API version:", ["2023-12-01-preview", "2023-05-15"], key="api_version", help="Select OpenAI API version used by your deployment.")
if model_provider == "Google AI API":
# Check if Google API key is in environment variables
google_api_key = os.getenv("GOOGLE_API_KEY")
if not google_api_key:
# Add Google API key input field to the sidebar if not in environment
st.session_state["GOOGLE_API_KEY"] = st.text_input(
"Enter your Google AI API key:",
type="password",
help="You can generate a Google AI API key in the [Google AI Studio](https://makersuite.google.com/app/apikey).",
)
else:
st.session_state["GOOGLE_API_KEY"] = google_api_key
st.success("API key loaded from .env")
# Add model selection input field to the sidebar
st.session_state["google_model"] = st.selectbox(
"Select the model you would like to use:",
["gemini-1.5-pro-latest", "gemini-1.5-pro"],
key="selected_model",
)
if model_provider == "Mistral API":
# Check if Mistral API key is in environment variables
mistral_api_key = os.getenv("MISTRAL_API_KEY")
if not mistral_api_key:
# Add Mistral API key input field to the sidebar if not in environment
st.session_state["MISTRAL_API_KEY"] = st.text_input(
"Enter your Mistral API key:",
type="password",
help="You can generate a Mistral API key in the [Mistral console](https://console.mistral.ai/api-keys/).",
)
else:
st.session_state["MISTRAL_API_KEY"] = mistral_api_key
st.success("API key loaded from .env")
# Add model selection input field to the sidebar
st.session_state["mistral_model"] = st.selectbox(
"Select the model you would like to use:",
["mistral-large-latest", "mistral-medium-latest", "mistral-small-latest", "open-mixtral-8x7b" ],
key="selected_model",
)
if model_provider == "Ollama":
# Make a request to the Ollama API to get the list of available models
try:
response = requests.get("http://localhost:11434/api/tags")
response.raise_for_status() # Raises a HTTPError if the status is 4xx, 5xx
except requests.exceptions.RequestException as e:
st.error("Ollama endpoint not found, please select a different model provider.")
response = None
if response:
data = response.json()
available_models = [model["name"] for model in data["models"]]
# Add model selection input field to the sidebar
ollama_model = st.selectbox(
"Select the model you would like to use:",
available_models,
key="selected_model",
)
st.session_state["ollama_model"] = ollama_model
st.markdown("""---""")
matrix = st.sidebar.radio(
"Select MITRE ATT&CK Matrix:",
["Enterprise", "ICS"],
key="selected_matrix"
)
st.session_state["matrix"] = matrix
# Add the drop-down selectors for Industry and Company Size
industry = st.selectbox(
"Select your company's industry:",
sorted(['Aerospace / Defense', 'Agriculture / Food Services',
'Automotive', 'Construction', 'Education',
'Energy / Utilities', 'Finance / Banking',
'Government / Public Sector', 'Healthcare',
'Hospitality / Tourism', 'Insurance',
'Legal Services', 'Manufacturing',
'Media / Entertainment', 'Non-profit',
'Real Estate', 'Retail / E-commerce',
'Technology / IT', 'Telecommunication',
'Transportation / Logistics'])
, placeholder="Select Industry")
st.session_state["industry"] = industry
company_size = st.selectbox("Select your company's size:", ['Small (1-50 employees)', 'Medium (51-200 employees)', 'Large (201-1,000 employees)', 'Enterprise (1,001-10,000 employees)', 'Large Enterprise (10,000+ employees)'], placeholder="Select Company Size")
st.session_state["company_size"] = company_size
st.sidebar.markdown("---")
st.sidebar.markdown("### <span style='color: #1DB954;'>About</span>", unsafe_allow_html=True)
st.sidebar.markdown("Created by [Matt Adams](https://www.linkedin.com/in/matthewrwadams)")
st.sidebar.markdown(
"⭐ Star on GitHub: [](https://github.com/mrwadams/attackgen)"
)
# ------------------ Main App UI ------------------ #
st.markdown("# <span style='color: #1DB954;'>AttackGen 👾</span>", unsafe_allow_html=True)
st.markdown("<span style='color: #1DB954;'> **Use MITRE ATT&CK and Large Language Models to generate attack scenarios for incident response testing.**</span>", unsafe_allow_html=True)
st.markdown("---")
st.markdown("""
### Welcome to AttackGen!
The MITRE ATT&CK framework is a powerful tool for understanding the tactics, techniques, and procedures (TTPs) used by threat actors; however, it can be difficult to translate this information into realistic scenarios for testing.
AttackGen solves this problem by using large language models to quickly generate attack scenarios based on a selection of a threat actor group's known techniques.
""")
if st.session_state.get('chosen_model_provider') == "Azure OpenAI Service":
st.markdown("""
### Getting Started
1. Enter the details of your Azure OpenAI Service model deployment, including the API key, endpoint, deployment name, and API version.
2. Select your industry and company size from the sidebar.
3. Go to the `Threat Group Scenarios` page to generate a scenario based on a threat actor group's known techniques, or go to the `Custom Scenarios` page to generate a scenario based on your own selection of ATT&CK techniques.
4. Use `AttackGen Assistant` to refine / update the generated scenario, or ask more general questions about incident response testing.
""")
elif st.session_state.get('chosen_model_provider') == "Google AI API":
st.markdown("""
### Getting Started
1. Enter your Google AI API key, then select your preferred model, industry, and company size from the sidebar.
2. Go to the `Threat Group Scenarios` page to generate a scenario based on a threat actor group's known techniques, or go to the `Custom Scenarios` page to generate a scenario based on your own selection of ATT&CK techniques.
3. Use `AttackGen Assistant` to refine / update the generated scenario, or ask more general questions about incident response testing.
""")
elif st.session_state.get('chosen_model_provider') == "Mistral API":
st.markdown("""
### Getting Started
1. Enter your Mistral API key, then select your preferred model, industry, and company size from the sidebar.
2. Go to the `Threat Group Scenarios` page to generate a scenario based on a threat actor group's known techniques, or go to the `Custom Scenarios` page to generate a scenario based on your own selection of ATT&CK techniques.
3. Use `AttackGen Assistant` to refine / update the generated scenario, or ask more general questions about incident response testing.
""")
elif st.session_state.get('chosen_model_provider') == "Ollama":
st.markdown("""
### Getting Started
1. Select your locally hosted model from the sidebar, then enter the details of the application you would like to threat model.
2. Go to the `Threat Group Scenarios` page to generate a scenario based on a threat actor group's known techniques, or go to the `Custom Scenarios` page to generate a scenario based on your own selection of ATT&CK techniques.
3. Use `AttackGen Assistant` to refine / update the generated scenario, or ask more general questions about incident response testing.
""")
else:
st.markdown("""
### Getting Started
1. Enter your OpenAI API key, then select your preferred model, industry, and company size from the sidebar.
2. Go to the `Threat Group Scenarios` page to generate a scenario based on a threat actor group's known techniques, or go to the `Custom Scenarios` page to generate a scenario based on your own selection of ATT&CK techniques.
3. Use `AttackGen Assistant` to refine / update the generated scenario, or ask more general questions about incident response testing.
""")