=
357 lines
19 KiB
Python
357 lines
19 KiB
Python
# Copyright (C) 2025 James Brotosky, Brandon Wickline
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import dotenv
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import os
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import utils.getdeviceevents
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import utils.allowlist
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import utils.hashfunctions
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import utils.pathfunctions
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import utils.pretty as ct
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import urllib3
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import pandas as pd
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import json
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import ast
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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dotenv.load_dotenv()
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url = "https://172.17.22.240:3129"
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badpublisherlist = ["Brave Software, Inc.", "Zoom Video Communications, Inc."]
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path_exclusion_constant = 3
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threat_tolerance_constant = 4
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def apivalidation():
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print(ct.colorText(r"""
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_____ .__ .__ __ ___________ .__
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/ _ \ |__|______| | ____ ____ | | __ \__ ___/___ ____ | | ______
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/ /_\ \| \_ __ \ | / _ \_/ ___\| |/ / | | / _ \ / _ \| | / ___/
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/ | \ || | \/ |_( <_> ) \___| < | |( <_> | <_> ) |__\___ \
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\____|__ /__||__| |____/\____/ \___ >__|_ \ |____| \____/ \____/|____/____ >
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\/ \/ \/ \/
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""", "cyan"))
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print(ct.colorText("=================================================================================", "cyan"))
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print(ct.colorText("======================== Welcome to the Airlock API Tool ========================", "cyan"))
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print(ct.colorText("=================================================================================", "cyan"))
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match os.getenv('APIKEY'):
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case '':
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print(ct.colorText("Please add your API Key to the .env file", "red"))
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case _:
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menu_main()
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def tryToReadCSV(csv):
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try:
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df =pd.read_csv(csv)
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if df.empty:
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print(ct.colorText("Error: CSV file has headers but no data rows.", "red"))
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else:
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print(ct.colorText(f"Data loaded successfully from {csv}", "green"))
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except pd.errors.EmptyDataError:
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print(ct.colorText("Notice : CSV file is completely empty (no headers, no data), falling back to empty frame", "white"))
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df = pd.DataFrame() # Create an empty DataFrame as fallback
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return df
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def menu_main():
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while True:
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print(ct.colorText("\n-----------------------------------", "magenta"))
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print(ct.colorText("------------ Main Menu ------------", "magenta"))
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print(ct.colorText("-----------------------------------", "magenta"))
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print(ct.colorText("1. Get All Events for Single Device", "yellow"))
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print(ct.colorText("2. Placeholder for Local Approval", "yellow"))
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print(ct.colorText("3. Placeholder for Another Tool", "yellow"))
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print(ct.colorText("4. Prepare Policy For Enforcement", "yellow"))
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print(ct.colorText("Q. Quit", "yellow"))
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choice = input(ct.colorText("\nEnter Menu Item: ", "white"))
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if choice == '1':
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utils.getdeviceevents.devicehistory(url,False)
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elif choice == "2":
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menu_local_approve()
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elif choice == "3":
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menu_feature2()
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elif choice == "4":
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menu_prepare_to_enforce()
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elif choice == "Q":
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break
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else:
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print(ct.colorText("Invalid choice. Please try again.","red"))
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def menu_local_approve():
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while True:
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print("\n--- Submenu ---")
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print("1. Sub-option A")
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print("2. Sub-option B")
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print("3. Return to Main Menu")
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choice = input("Enter your choice: ")
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if choice == "1":
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print("You selected Sub-option A")
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elif choice == "2":
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print("You selected Sub-option B")
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elif choice == "3":
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print("Returning to Main Menu...")
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break
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else:
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print("Invalid choice. Please try again.")
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def menu_feature2():
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while True:
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print("\n--- Submenu ---")
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print("1. Sub-option A")
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print("2. Sub-option B")
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print("3. Return to Main Menu")
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choice = input("Enter your choice: ")
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if choice == "1":
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print("You selected Sub-option A")
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elif choice == "2":
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print("You selected Sub-option B")
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elif choice == "3":
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print("Returning to Main Menu...")
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break
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else:
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print("Invalid choice. Please try again.")
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def menu_prepare_to_enforce():
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first_policy = " "
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second_policy = " "
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#If the directorys where we're going to store our output dont exist, make them.
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if not os.path.exists("dataframe_html"): os.makedirs("dataframe_html")
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if not os.path.exists("dataframe_csv"): os.makedirs("dataframe_csv")
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if not os.path.exists("manuallyapproved"): os.makedirs("manuallyapproved")
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if not os.path.exists("preflight"): os.makedirs("preflight")
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df_aggregated_combo = pd.DataFrame()
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while True:
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print(ct.colorText("\n --------------------------------------------------------------------", "cyan"))
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print(ct.colorText(" -------------------- Prepare to Enforce Policy ---------------------", "cyan"))
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print(ct.colorText(" --------------------------------------------------------------------", "cyan"))
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print(ct.colorText("\nSequentually follow these steps to prepare a policy for enforcement:", "white"))
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print(ct.colorText("\n1. Choose which policy or policies to work with - : ", "cyan"))
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if first_policy == " " and second_policy == " ":
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print(ct.colorText(f" [✗] No policies have been chosen","red"))
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elif first_policy != " " and second_policy is first_policy:
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print(ct.colorText(f" [✓] {first_policy} has been selected,", "green"))
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elif first_policy != " " and second_policy != " ":
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print(ct.colorText(f" [✓] {first_policy} has been selected as Policy 1","green"))
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print(ct.colorText(f" [✓] {second_policy} has been selected as Policy 2","green"))
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print(ct.colorText("2. Pulls and stages event history, combines the histories, adds hash info, then categorizes the hashes", "cyan"))
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if os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
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print(ct.colorText(f" [✓] Execution history has been compiled for {first_policy}","green"))
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elif not os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
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print(ct.colorText(f" [✗] Execution history has not been compiled for {first_policy}","red"))
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elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
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print(ct.colorText(f" [✓] Execution history has been compiled for {second_policy}","green"))
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elif second_policy is not first_policy and not os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
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print(ct.colorText(f" [✗] Execution history has not been compiled for {second_policy}","red"))
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if os.path.exists(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv"):
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print(ct.colorText(f" [✓] Execution history has been_combined_for {first_policy} and_{second_policy}", "green"))
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else:
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print(ct.colorText(f" [✗] Execution history has not been_combined_for {first_policy} and_{second_policy}", "red"))
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if os.path.exists(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv"):
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print(ct.colorText(f" [✓] Hash Info has been added to the combined execution history", "green"))
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else:
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print(ct.colorText(f" [✗] Hash Info has not been added to the combined execution history", "red"))
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if os.path.exists(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv"):
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print(ct.colorText(f" [✓] Hashes have been cateogrized", "green"))
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else:
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print(ct.colorText(f" [✗] Hashes have not been cateogrized", "red"))
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print(ct.colorText(f"3. Manually review the files '\\dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv' and 'dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv'", "cyan"))
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print(ct.colorText(" Remove the rows containing hashes you do not approve of, and those you would not approve of without metarules.", "cyan"))
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print(ct.colorText(" If metarules need to be created, please make note of them, and remove the row from the csv.", "cyan"))
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print(ct.colorText(" When complete, save both csv files to the directory 'manuallyapproved' and choose this option to combine these approved hashes with the automatically approved hashes and generate a list of paths to be reviewed", "cyan"))
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if os.path.isfile(f"dataframe_csv\\paths_needing_review_{first_policy}_{second_policy}.csv"):
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText(f"4. Manually review the file 'paths_needing_review_{first_policy}_{second_policy}.csv'", "cyan"))
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print(ct.colorText(" Remove the rows containing path exclusions you do not approve of" , "cyan"))
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print(ct.colorText(" When complete, save the csv file to the directory 'manuallyapproved' and choose this option to generate the preflight lists", "cyan"))
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if os.path.isfile(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile("dataframe_csv\\hashdestination_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtochildpolicy_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtobaseline_{first_policy}_{second_policy}.csv"):
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText("Q. Quit", "cyan"))
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choice = input(ct.colorText("\nEnter your choice: ", "white"))
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if choice == "1":
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first_policy_tuple = utils.allowlist.listPolicies(url)
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first_policy = first_policy_tuple[1][first_policy_tuple[0]]
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while True:
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answer = input(ct.colorText(f"{"Do you want to load a second policy?"} (yes/no): ", "white").strip().lower())
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if answer in ("yes", "y"):
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second_policy_tuple = utils.allowlist.listPolicies(url)
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second_policy = second_policy_tuple[1][second_policy_tuple[0]]
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break
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elif answer in ("no", "n"):
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second_policy_tuple = first_policy_tuple
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second_policy = first_policy
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break
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else:
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print(ct.colorText("Please answer with 'yes' or 'no'.", "red"))
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elif choice == "2":
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if not os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
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exe1 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
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data = json.loads(exe1)
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executionhist_policy1 = pd.DataFrame(data["response"]["exechistories"])
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executionhist_policy1.to_csv(f"dataframe_csv\\executionhist_{first_policy}.csv", index=False)
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ct.style_dataframe_dark(executionhist_policy1, f"dataframe_html\\executionhist_{first_policy}.html")
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print(ct.colorText(f"Staging of Execution history for policy: {first_policy} is complete","green"))
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if not os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
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exe2 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
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data2 = json.loads(exe2)
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executionhist_policy2 = pd.DataFrame(data2["response"]["exechistories"])
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executionhist_policy2.to_csv(f"dataframe_csv\\executionhist_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(executionhist_policy2, f"dataframe_html\\executionhist_{second_policy}.html")
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print(ct.colorText(f"Staging of Exection history for policy: {first_policy} is complete","green"))
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#Combine the two policies execution histories
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if second_policy is first_policy:
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execuctionhist_combined = executionhist_policy1
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execuctionhist_combined.to_csv(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\execuctionhist_combined_{first_policy}_{second_policy}.html")
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print(ct.colorText(f"Dataframes have been combined","green"))
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elif os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv") and os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
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execuctionhist_combined = pd.concat([tryToReadCSV(f"dataframe_csv\\executionhist_{first_policy}.csv") , tryToReadCSV(f"dataframe_csv\\executionhist_{second_policy}.csv")], ignore_index=True)
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execuctionhist_combined.to_csv(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\execuctionhist_combined_{first_policy}_{second_policy}.html")
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print(ct.colorText(f"Dataframes have been combined","green"))
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#Keep only unique combinations of hash, filename, and hostname
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if f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv":
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unique_executions = tryToReadCSV(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv").drop_duplicates(subset=['sha256', 'filename', 'hostname'])
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unique_executions.to_csv(f"dataframe_csv\\unique_executions{first_policy}_{second_policy}.csv")
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ct.style_dataframe_dark(unique_executions, f"dataframe_html\\unique_execuctions.html")
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#Add Hash info to the combined execution history
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if not os.path.exists(f"dataframe_html\\augmented_combo_{first_policy}_{second_policy}.html"):
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print(ct.colorText(f"Preparing to pull hash info","green"))
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augmented_combo= utils.hashfunctions.augmentAggregatedHashes(url,tryToReadCSV(f"dataframe_csv\\unique_executions{first_policy}_{second_policy}.csv"))
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augmented_combo.to_csv(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(augmented_combo, f"dataframe_html\\augmented_combo_{first_policy}_{second_policy}.html")
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print(ct.colorText(f"Hash reputation info added to dataframe","green"))
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#Categorize the hashes
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if os.path.exists(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv"):
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break
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else:
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categorized = utils.hashfunctions.categorizeHashes(pd.read_csv(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv"), threat_tolerance_constant, badpublisherlist)
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categorized[0].to_csv(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(categorized[0], f"dataframe_html\\dashes_needing_approval_{first_policy}_{second_policy}.html")
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categorized[1].to_csv(f"dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(categorized[1], f"dataframe_html\\automatically_approved_hashes_{first_policy}_{second_policy}.html")
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categorized[2].to_csv(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(categorized[2], f"dataframe_html\\unapproved_hashes_{first_policy}_{second_policy}.html")
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print(ct.colorText(f"Hashes have been categorized","green"))
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elif choice == "3":
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if os.path.exists(f"manuallyapproved\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"manuallyapproved\\automatically_approved_hashes_{first_policy}_{second_policy}.csv"):
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df1 = tryToReadCSV(f"manuallyapproved\\hashes_needing_approval_{first_policy}_{second_policy}.csv")
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df2 = tryToReadCSV(f"manuallyapproved\\automatically_approved_hashes_{first_policy}_{second_policy}.csv")
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df_all_approved_hashes = pd.concat([df1 , df2], ignore_index=True)
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df_all_approved_hashes.to_csv(f"dataframe_csv\\all_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_all_approved_hashes, f"dataframe_html\\all_approved_hashes_{first_policy}_{second_policy}.html")
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df_paths_needing_review, df_path_ineligible = utils.pathfunctions.filepathInitialGroup(pd.read_csv(f"dataframe_csv\\all_approved_hashes_{first_policy}_{second_policy}.csv"))
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df_paths_needing_review.to_csv(f"dataframe_csv\\paths_needing_review_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_paths_needing_review, f"dataframe_html\\paths_needing_review_{first_policy}_{second_policy}.html")
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df_path_ineligible.to_csv(f"dataframe_csv\\path_ineligible_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_path_ineligible, f"dataframe_html\\path_ineligible_{first_policy}_{second_policy}.html")
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print(ct.colorText(f"Eligible paths determined","green"))
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else:
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print(ct.colorText(f"Please manually approve hashes prior to this step","red"))
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elif choice == "4":
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if os.path.exists(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv"):
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df_approved = tryToReadCSV(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv")
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df_eligible = tryToReadCSV(f"dataframe_csv\\paths_needing_review_{first_policy}_{second_policy}.csv")
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df_ineligible = tryToReadCSV(f"dataframe_csv\\path_ineligible_{first_policy}_{second_policy}.csv")
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approved_set = set([tuple(map(tuple, row)) for row in df_approved.values])
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# Identify rows in eligible that are not in approved
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not_approved_rows = df_eligible[~df_eligible.apply(lambda row: tuple(map(tuple, row)) in approved_set, axis=1)]
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# Append these rows to ineligible
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df_ineligible= pd.concat([df_ineligible, not_approved_rows], ignore_index=True)
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df_approved.to_csv(f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.csv")
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ct.style_dataframe_dark(df_approved, f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.html")
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#Seperate out what we arent excluding by path into those that will go into the baseline, and those that will b added to the child.
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df_addtobaseline = df_ineligible[df_ineligible['reputation_status'] == 'KNOWN']
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df_addtobaseline.to_csv(f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_addtobaseline, f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.html")
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df_addtochildpolicy = df_ineligible[df_ineligible['reputation_status'] == 'UNKNOWN']
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df_addtochildpolicy.to_csv(f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(df_addtochildpolicy, f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.html")
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else:
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print(ct.colorText(f"Please manually approve suggested paths prior to this step","red"))
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elif choice == "Q":
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break
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else:
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print(ct.colorText("Invalid choice. Please try again.", "red"))
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if __name__ == "__main__":
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apivalidation()
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