-
Notifications
You must be signed in to change notification settings - Fork 149
/
Copy path00_👋_Welcome.py
302 lines (251 loc) · 14.9 KB
/
00_👋_Welcome.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
"""
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", "Groq 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", "o3-mini", "o1", "o1-mini"],
key="selected_model",
help="GPT-4o and GPT-4o mini are OpenAI's latest models and are recommended. o3 and o1 are reasoning 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 == "Groq API":
# Check if Groq API key is in environment variables
groq_api_key = os.getenv("GROQ_API_KEY")
if not groq_api_key:
# Add Groq API key input field to the sidebar if not in environment
st.session_state["GROQ_API_KEY"] = st.text_input(
"Enter your Groq API key:",
type="password",
help="You can find your Groq API key in the [Groq Console](https://console.groq.com/keys).",
)
else:
st.session_state["GROQ_API_KEY"] = groq_api_key
st.success("API key loaded from .env")
# Add model selection input field to the sidebar
st.session_state["groq_model"] = st.selectbox(
"Select the model you would like to use:",
["llama-3.3-70b-versatile", "deepseek-r1-distill-llama-70b"],
key="selected_model",
help="Llama 3.3 70B is recommended for best performance. DeepSeek R1 is a reasoning model available for testing.",
)
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.
""")
elif st.session_state.get('chosen_model_provider') == "Groq API":
st.markdown("""
### Getting Started
1. Enter your Groq 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.
""")
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.
""")