Zarithas
89db386ffe
- Migrated codebase to class-based architecture for better modularity and maintainability - Introduced system_config.json for centralized configuration (required for runtime) - Added structured working directories for improved file organization - Significantly reduced reliance on Parquet; replaced with alternative data handling - Implemented security improvements across modules - Several TODOs remain in the main script for future enhancements - Linter formatting affected readability in some files (e.g., utils); cleanup is on the agenda
293 lines
11 KiB
Python
293 lines
11 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 datetime
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import logging
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import os
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import re
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import time
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import dotenv
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import numpy as np
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import pandas as pd
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from models.agent import Agent
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from services.agenthandler import findAllAgents, moveAgentToRelatedPolicy, selectAgents
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from services.API import AirlockAPIWrapper
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from services.scheduler import (
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register_function,
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run_once_job,
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)
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from utils.utils import colorText, load_env, load_env_json
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logger = logging.getLogger(__name__)
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dotenv.load_dotenv()
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def getLocalApprovals(api: AirlockAPIWrapper):
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working_dir = load_env("WORKING_DIR")
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result = api.otp_find_awaiting()
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local_approval = pd.DataFrame(result["response"]["otpusage"])
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if os.path.exists(f"{working_dir}\\Scheduling\\newest_local_approval.parquet"):
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previous_run = pd.read_parquet(f"{working_dir}\\Scheduling\\newest_local_approval.parquet")
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previous_run.to_parquet(
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f"{working_dir}\\Scheduling\\last_local_approval.parquet", index=False
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)
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os.remove(f"{working_dir}\\Scheduling\\newest_local_approval.parquet")
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# Only keep rows presumably created by the generate local approval function
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local_approval = local_approval[
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local_approval["purpose"].str.startswith("🎫 Local Approval 🎫")
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]
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local_approval["batchid"] = local_approval["purpose"].apply(
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lambda x: (match := re.search(r"batch:(\S+)", str(x))) and match.group(1)
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)
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if not local_approval.empty:
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local_approval.to_parquet(
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f"{working_dir}\\Scheduling\\newest_local_approval.parquet", index=False
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)
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return local_approval
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def scheduleAddingLAHashes(api: AirlockAPIWrapper):
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policy_relationship_map = load_env_json("POLICY_MAP_ENF_AUD","{}")
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bad_publisher_list = load_env_json("BAD_PUBLISHER", "[]")
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pups = load_env_json("PUPS", "[]")
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threat_tolerance_constant = load_env("VT_THREAT_TOLERANCE", cast_type = int)
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try:
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register_function("add_hash", returnFromLocalApproval)
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register_function("move_device", moveAgentToRelatedPolicy)
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except Exception as e:
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logger.warning(f"Failed to register functions: {e}")
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return
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try:
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approvals_df = getNewLocalApprovals(api)
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if approvals_df.empty:
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logger.debug("No new local approvals found. Nothing to schedule.")
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return
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batches = approvals_df.groupby("batchid")
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except Exception as e:
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logger.warning(f"Failed to retrieve or group local approvals: {e}")
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return
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for batchid, batch_df in batches:
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try:
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duration_minutes = int(batch_df["duration"].iloc[0])
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start_time = datetime.datetime.now()
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run_time = start_time + datetime.timedelta(minutes=duration_minutes)
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early_time = start_time + datetime.timedelta(minutes=np.floor(duration_minutes * 0.95))
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early_timestamp = early_time.timestamp()
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run_timestamp = run_time.timestamp()
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# Schedule add_hash job
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try:
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run_once_job(
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f"add_hash_{batchid}",
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"add_hash",
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early_timestamp,
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[
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api,
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batch_df,
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policy_relationship_map,
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bad_publisher_list,
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pups,
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threat_tolerance_constant,
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],
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None,
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)
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logger.debug(f"Scheduled add_hash for batch {batchid} at {early_time}")
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except Exception:
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logger.debug("Failed to schedule add_hash for batch {batchid}: {e}")
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# Schedule move_device jobs
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devices = batch_df["agentid"].drop_duplicates().tolist()
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agents = []
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for device in devices:
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rows = api.agent_find_by_hostname(device).iterrows()
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agents += [Agent(**row["data"]) for _, row in rows]
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for agent in agents:
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try:
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run_once_job(
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f"move_device_{agent.hostame}_{batchid}",
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"move_device",
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run_timestamp,
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[api, agent, policy_relationship_map],
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"enforcement",
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)
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print(
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f"Scheduled move_device for device {agent.hostname} in batch {batchid} at {run_time}"
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)
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except Exception as e:
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print(
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f"Failed to schedule move_device for device {agent.hostname} in batch {batchid}: {e}"
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)
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except Exception as e:
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logger.warning(f"Failed to process batch {batchid}: {e}")
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def returnFromLocalApproval(api, device_df, policy_relationship_map, bad_publisher_list, pups, threat_tolerance_constant
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):
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"""
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# Get unique policy names from device list
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policies_in_devicelist = sorted(device_df['policy_name'].unique().tolist())
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# Create inverse map to go from Audit to Enforcement
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inverse_map = {v: k for k, v in policy_relationship_map.items()}
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# Fetch all policies
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all_policies = [Policy(row['groupid'], row['hidden'], row['name'], row['parent']) for _, row in api.policy_find_all().iterrows()]
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# Define policy types
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policy_types = [1, 2, 6, 7]
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#TODO finish logic for adding hashes
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"""
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working_dir = load_env("WORKING_DIR")
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policy_relationship_map = load_env_json("POLICY_MAP_ENF_AUD","{}")
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bad_publisher_list = load_env_json("BAD_PUBLISHER", "[]")
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pups = load_env_json("PUPS", "[]")
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threat_tolerance_constant = load_env("VT_THREAT_TOLERANCE")
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print(f"{working_dir}, {policy_relationship_map}, {bad_publisher_list}, {pups}, {threat_tolerance_constant}")
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def moveToLocalApproval(api: AirlockAPIWrapper):
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possible_durations = [15, 60, 360, 1440, 10080]
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duration_selected = None
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print(colorText("Please select a duration:", "white"))
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for i, option in enumerate(possible_durations, start=1):
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print(f"{i}. {option}")
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try:
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choice = int(input("Enter the number of your choice: "))
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if 1 <= choice <= len(possible_durations):
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duration_selected = possible_durations[choice - 1]
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print(colorText(f"You selected: {duration_selected}", "yellow"))
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logger.debug(f"You selected: {duration_selected}")
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else:
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print(colorText("❌ Invalid choice.", "red"))
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logger.debug("Invalid Input")
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return
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except ValueError:
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print(colorText("❌ Invalid input. Please enter a number.", "red"))
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logger.debug("Invalid Input")
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return
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agents = selectAgents(api)
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batch = int(time.time())
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if not agents:
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print(colorText("❌ No agents found or error retrieving agents.", "red"))
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logger.debug("No agents found or error retrieving agents")
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return
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for agent in agents:
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try:
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addLocalApproval(api, batch, duration_selected, agent.agentid)
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moveAgentToRelatedPolicy(api, agent, "audit")
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except Exception as e:
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print(colorText(f"❌ Error processing agent {agent.hostname}: {e}", "red"))
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def addLocalApproval(api: AirlockAPIWrapper, batchid, duration_selected, agentid):
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purpose = f"🎫 Local Approval 🎫 - {duration_selected} mins - batch:{batchid} Client:{agentid}"
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api.otp_generate(agentid, duration_selected, purpose)
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def monitorAuditStatus(api: AirlockAPIWrapper):
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current_agents = findAllAgents(api)
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last_agents = []
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if not last_agents:
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last_agents = current_agents
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policy_relationship_map = load_env_json("POLICY_MAP_ENF_AUD","{}")
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# Reverse map for audit → enforcement
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reverse_policy_map = {v: k for k, v in policy_relationship_map.items()}
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known_transitions = set(policy_relationship_map.items()) | set(reverse_policy_map.items())
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# Index last_agents by hostname for quick lookup
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last_agent_map = {agent.hostname: agent for agent in last_agents}
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# Result buckets
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newly_added = []
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same_policy = []
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moved_to_audit = []
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moved_to_enforcement = []
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unusual_move = []
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for current in current_agents:
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previous = last_agent_map.get(current.hostname)
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if not previous:
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newly_added.append(current)
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continue
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if current.groupid == previous.groupid:
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same_policy.append(current)
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elif (previous.groupid, current.groupid) in known_transitions:
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moved_to_audit.append(current)
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elif (current.groupid, previous.groupid) in known_transitions:
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moved_to_enforcement.append(current)
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else:
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unusual_move.append(current)
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# Return all five DataFrames
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return newly_added, same_policy, moved_to_audit, moved_to_enforcement, unusual_move
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def getNewLocalApprovals(api: AirlockAPIWrapper):
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working_dir = load_env("WORKING_DIR")
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current_la = getLocalApprovals(api)
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# Load old approval list
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old_la_path = f"{working_dir}\\Scheduling\\last_local_approval.parquet"
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if os.path.exists(old_la_path):
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old_la = pd.read_parquet(old_la_path)
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else:
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old_la = pd.DataFrame(columns=current_la.columns)
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# Create composite keys
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current_la["key"] = current_la["clientid"].astype(str) + "_" + current_la["granted"].astype(str)
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old_la["key"] = old_la["clientid"].astype(str) + "_" + old_la["granted"].astype(str)
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# Find new entries
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new_entries = current_la[~current_la["key"].isin(old_la["key"])]
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# Convert 'granted' to datetime and filter by last 10 minutes
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new_entries["granted"] = pd.to_datetime(new_entries["granted"], utc=True, errors="coerce")
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ten_minutes_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(minutes=10)
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recent_entries = new_entries[new_entries["granted"] > ten_minutes_ago]
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# Save current approvals for next run
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current_la.drop(columns=["key"], inplace=True)
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current_la.to_parquet(old_la_path, index=False)
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return recent_entries
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