First Round Async. Much work left to do, dont trust results of hash categorization presently.
This commit is contained in:
+181
-185
@@ -13,10 +13,11 @@
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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 asyncio
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import logging
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import os
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import os.path
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from typing import List
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from typing import List, Optional
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import dotenv
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import pandas as pd
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@@ -24,12 +25,12 @@ import pandas as pd
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from models.execution import ExecutionHistoryRecord, Hash
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from models.policy import Allowlist, Policy
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from services.API import AirlockAPIWrapper
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from services.TaskQueue import AsyncTaskQueue, run_sync_task_in_thread
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from utils.configmanager import get_protected_value, load_env, load_env_json
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from utils.selector import Selector
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from utils.Selector import Selector
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from utils.utils import (
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colorText,
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formatHTML,
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import_to_dataframe,
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regulator,
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)
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@@ -37,79 +38,86 @@ logger = logging.getLogger(__name__)
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dotenv.load_dotenv()
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async def selectPolicies(api: AirlockAPIWrapper, allow_multiple=True) -> List[Policy]:
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df = await api.policy_find_all()
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policies = [Policy(**row.to_dict()) for _, row in df.iterrows()]
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def selectPolicies(api: AirlockAPIWrapper, allow_multiple=True) -> List[Policy]:
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policies = [Policy(**row.to_dict()) for _, row in api.policy_find_all().iterrows()]
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logger.debug("Prompting for Policies")
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print(colorText("Please select policy/policies", "white"))
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selected = Selector.select_objects(policies, allow_multiple, prompt_each=True)
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selected = await Selector.select_objects(policies, allow_multiple, prompt_each=True)
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if selected is None:
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return []
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# Normalize to always return a list
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logger.debug("Returning {selected.dict}")
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logger.debug("Returning selected policies")
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return selected if isinstance(selected, list) else [selected]
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def selectAllowlists(api: AirlockAPIWrapper, policy = all, allow_multiple=True) -> List[Allowlist]:
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if policy == "all": allowlists = [Allowlist(**row.to_dict()) for _, row in api.allowlist_find_all().iterrows()]
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else: allowlists = [Allowlist(**row.to_dict()) for _, row in api.policy_list_allowlists(policy[0].groupid).iterrows()]
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async def selectAllowlists(api: AirlockAPIWrapper, policy="all", allow_multiple=True) -> List[Allowlist]:
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if policy == "all":
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df = await api.allowlist_find_all()
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else:
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df = await api.policy_list_allowlists(policy[0].groupid) # pyright: ignore[reportAttributeAccessIssue]
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allowlists = [Allowlist(**row.to_dict()) for _, row in df.iterrows()]
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logger.debug("Prompting for Allowlist(s)")
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print(colorText("Please select allowlist(s)", "white"))
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selected = Selector.select_objects(allowlists, allow_multiple, prompt_each=True)
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selected = await Selector.select_objects(allowlists, allow_multiple, prompt_each=True)
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if selected is None:
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return []
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# Normalize to always return a list
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logger.debug(f"Returning {selected}")
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return selected if isinstance(selected, list) else [selected]
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def sortHashes(
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async def sortHashes(
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api: AirlockAPIWrapper,
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queue: AsyncTaskQueue,
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selected_policies: List[Policy],
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type=[1, 2, 6, 7]
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type=[1, 2, 6, 7],
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history_days: Optional[int] = None
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):
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working_dir = load_env("WORKING_DIR")
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history_days = Selector.select_value(
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prompt="Enter how many days of history to pull (1–150): ",
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value_type=int,
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valid_range=(1, 150),
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)
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if history_days is None:
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history_days = await Selector.select_value(
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prompt="Enter how many days of history to pull (1–150): ",
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value_type=int,
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valid_range=(1, 150),
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)
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logger.debug(f"{history_days} day selected for history")
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if history_days is None:
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logging.warning("No history range selected. Aborting.")
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return
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executions = []
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hashes = []
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working_dir = await load_env("WORKING_DIR")
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# Pull execution histories for each policy
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policy_executions = ExecutionHistoryRecord.from_policies(
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policy_executions = await ExecutionHistoryRecord.from_policies(
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api, selected_policies, type_=type, history_days=history_days
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)
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logger.debug(f"Policy_executions is {policy_executions}")
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logger.debug(f"Policy_executions is {policy_executions}")
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executions.extend(policy_executions)
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logger.debug(f"Executions contains {executions}")
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if executions:
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hashes = [Hash(sha256=row["sha256"], **row["data"]) for _, row in api.hash_query([record.sha256 for record in executions]).iterrows()
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sha_list = [record.sha256 for record in executions]
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hash_df = await api.hash_query(sha_list)
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hashes = [
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Hash(sha256=row["sha256"], **row["data"])
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for _, row in hash_df.iterrows()
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]
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if hashes:
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unique_hashes = Hash.deduplicate(hashes)
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needs_review, approved, unapproved = Hash.categorize_hashes(
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hashes=unique_hashes
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)
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needs_review, approved, unapproved = await Hash.categorize_hashes(hashes=unique_hashes)
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categories = {
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"needs_review": needs_review,
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@@ -118,157 +126,158 @@ def sortHashes(
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}
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for label, category in categories.items():
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csv_path = f"{working_dir}\\Needs_Review\\Review_First\\{label}_executions.csv"
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html_path = f"{working_dir}\\Needs_Review\\HTML\\{label}.html"
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csv_path = f"{working_dir}/Needs_Review/Review_First/{selected_policies[0].name}_{label}_executions.csv"
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html_path = f"{working_dir}/Needs_Review/HTML/{selected_policies[0].name}_{label}.html"
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ExecutionHistoryRecord.enrich_with_hashes_and_export(
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executions, category, f"{working_dir}\\Needs_Review\\Review_First", label=label
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)
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df = import_to_dataframe(csv_path)
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formatHTML(df, html_path)
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df = await ExecutionHistoryRecord.enrich_with_hashes(executions, category)
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asyncio.create_task(queue.enqueue(
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f"DF TO CSV {selected_policies[0].name}_{label}",
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run_sync_task_in_thread,
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df.to_csv,
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csv_path,
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index=False,
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encoding='utf-8'
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))
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def buildPathsandPublishers(split):
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working_dir = load_env("WORKING_DIR")
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df1 = pd.DataFrame()
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df2 = pd.DataFrame()
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all_approved_hashes = pd.DataFrame()
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path1 = f"{working_dir}\\Approved\\approved_executions.csv"
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path2 = f"{working_dir}\\Approved\\needs_review_executions.csv"
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if os.path.exists(path1):
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df1 = pd.read_csv(path1)
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else:
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logger.warning(f"File not found: {path1}")
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asyncio.create_task(queue.enqueue(
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f"DF TO HTML {selected_policies[0].name}_{label}",
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run_sync_task_in_thread,
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formatHTML,
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df,
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html_path
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))
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if os.path.exists(path2):
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df2 = pd.read_csv(path2)
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else:
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logger.warning(f"File not found: {path2}")
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print("sortHashes completed successfully.")
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async def buildPathsandPublishers(queue: AsyncTaskQueue, split):
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working_dir = await load_env("WORKING_DIR")
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path1 = f"{working_dir}/Approved/approved_executions.csv"
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path2 = f"{working_dir}/Approved/needs_review_executions.csv"
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df1 = await asyncio.to_thread(pd.read_csv, path1) if os.path.exists(path1) else pd.DataFrame()
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if df1.empty:
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logger.warning(f"File not found or empty: {path1}")
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df2 = await asyncio.to_thread(pd.read_csv, path2) if os.path.exists(path2) else pd.DataFrame()
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if df2.empty:
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logger.warning(f"File not found or empty: {path2}")
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if df1.empty and df2.empty:
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logger.warning("Both DataFrames are empty. Skipping sort.")
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all_approved_hashes = pd.DataFrame()
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logger.debug(all_approved_hashes.head)
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logger.debug(all_approved_hashes.head())
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return
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all_approved_hashes = pd.concat([df1, df2], ignore_index=True)
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if "filename_exec" in all_approved_hashes.columns:
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all_approved_hashes = all_approved_hashes.sort_values(by="filename_exec")
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else:
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all_approved_hashes = pd.concat([df1, df2], ignore_index=True)
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if "filename_exec" in all_approved_hashes.columns:
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all_approved_hashes = all_approved_hashes.sort_values(by="filename_exec")
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else:
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logger.warning("Warning: 'filename_exec' column not found in concatenated DataFrame.")
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logger.warning("'filename_exec' column not found in concatenated DataFrame.")
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if not all_approved_hashes.empty:
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primary_path_exclusions = calculatePath(
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all_approved_hashes,
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split,
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)
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remaining_hashes = all_approved_hashes[
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~all_approved_hashes["sha256"].isin(primary_path_exclusions["sha256"])
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]
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secondary_path_exclusions = calculatePath(
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remaining_hashes, split
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)
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remaining_hashes = remaining_hashes[
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~remaining_hashes["sha256"].isin(secondary_path_exclusions["sha256"])
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]
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dataframes = {
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"primary_Paths": primary_path_exclusions,
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"secondary_Paths": secondary_path_exclusions,
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"hashes_to_add": remaining_hashes,
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}
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logger.debug("Preparing to sort dataframes")
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for name, df in dataframes.items():
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logger.debug(f" DataFrame headers: {list(df.columns)}")
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if name == "hashes_to_add": df.sort_values(by="filename_exec", inplace=True)
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else: df.sort_values(by="longestcfp", inplace=True)
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df.to_csv(f"{working_dir}\\Needs_Review\\Review_Second\\{name}.csv", index=False)
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formatHTML(df, f"{working_dir}\\Needs_Review\\HTML\\{name}.html")
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primary_path_exclusions = await calculatePath(all_approved_hashes, split)
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remaining_hashes = all_approved_hashes[~all_approved_hashes["sha256"].isin(primary_path_exclusions["sha256"])]
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secondary_path_exclusions = await calculatePath(remaining_hashes, split)
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remaining_hashes = remaining_hashes[~remaining_hashes["sha256"].isin(secondary_path_exclusions["sha256"])]
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if not all_approved_hashes.empty:
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# Drop all not signed, only keep unique values
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publist = all_approved_hashes[
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all_approved_hashes["publisher_hash"] != "Not Signed"
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].drop_duplicates(subset=["publisher_hash"])
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# Remove Bad publisher if somehow they made it this far
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pattern = regulator(load_env_json("BAD_PUBLISHERS","[]"))
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publist = publist[~publist["publisher_hash"].str.contains(pattern, na=False)]
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publist = publist[["publisher_hash"]]
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publist.sort_values(by="publisher_hash", inplace=True)
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publist.to_csv(f"{working_dir}\\Needs_Review\\Review_Second\\publishers.csv", index=False)
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def buildPreflights():
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working_dir = load_env("WORKING_DIR")
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df1 = pd.DataFrame()
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df2 = pd.DataFrame()
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approved_hashes = pd.DataFrame()
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approved_publishers = pd.DataFrame()
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hash = f"{working_dir}\\Approved\\hashes_to_add.csv"
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path1 = f"{working_dir}\\Approved\\primary_Paths.csv"
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path2 = f"{working_dir}\\Approved\\secondary_Paths.csv"
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publishers = f"{working_dir}\\Approved\\publishers.csv"
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if os.path.exists(hash):
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approved_hashes = pd.read_csv(hash)
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else:
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logger.warning(f"File not found: {hash}")
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if os.path.exists(path1):
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df1 = pd.read_csv(path1)
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else:
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logger.warning(f"File not found: {path1}")
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if os.path.exists(path2):
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df2 = pd.read_csv(path2)
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else:
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logger.warning(f"File not found: {path2}")
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if df1.empty and df2.empty:
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logger.warning("Both DataFrames are empty. Skipping sort.")
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approved_paths = pd.DataFrame()
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else:
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approved_paths = pd.concat([df1, df2], ignore_index=True)
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if os.path.exists(publishers):
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approved_publishers = pd.read_csv(publishers)
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else:
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logger.warning(f"File not found: {publishers}")
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dataframes = {"approved_paths": approved_paths, "approved_hashes": approved_hashes, "approved_publishers": approved_publishers}
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dataframes = {
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"primary_Paths": primary_path_exclusions,
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"secondary_Paths": secondary_path_exclusions,
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"hashes_to_add": remaining_hashes,
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}
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for name, df in dataframes.items():
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logger.debug(f" DataFrame headers: {list(df.columns)}")
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if name == "approved_paths":df.sort_values(by="longestcfp", inplace=True)
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elif name == "approved_hashes":df.sort_values(by="filename_exec", inplace=True)
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elif name == "approved_publishers" : df.sort_values(by="publisher_hash", inplace=True)
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df.to_csv(f"{working_dir}\\Preflight\\{name}.csv", index=False)
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formatHTML(df, f"{working_dir}\\Preflight\\HTML\\{name}.html")
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logger.debug(f"DataFrame headers for {name}: {list(df.columns)}")
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sort_column = "filename_exec" if name == "hashes_to_add" else "longestcfp"
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df.sort_values(by=sort_column, inplace=True)
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csv_path = f"{working_dir}/Needs_Review/Review_Second/{name}.csv"
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html_path = f"{working_dir}/Needs_Review/HTML/{name}.html"
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await asyncio.to_thread(df.to_csv, csv_path, index=False)
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await asyncio.to_thread(formatHTML, df, html_path)
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def splitFilepathsGrouped(df, col="filename"):
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path_exclusion_constant = get_protected_value("PATH_EXCLUSION_CONST", cast_type= int)
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min_files_for_path = get_protected_value("MIN_FILES_FOR_PATH", cast_type= int)
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publist = all_approved_hashes[all_approved_hashes["publisher_hash"] != "Not Signed"].drop_duplicates(subset=["publisher_hash"])
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pattern = regulator(await load_env_json("BAD_PUBLISHERS", "[]"))
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publist = publist[~publist["publisher_hash"].str.contains(pattern, na=False)]
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publist = publist[["publisher_hash"]]
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publist.sort_values(by="publisher_hash", inplace=True)
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pub_csv_path = f"{working_dir}/Needs_Review/Review_Second/publishers.csv"
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await asyncio.to_thread(publist.to_csv, pub_csv_path, index=False)
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print("buildPathsandPublishers completed asynchronously.")
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async def buildPreflights():
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working_dir = await load_env("WORKING_DIR")
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hash_path = f"{working_dir}/Approved/hashes_to_add.csv"
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path1 = f"{working_dir}/Approved/primary_Paths.csv"
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path2 = f"{working_dir}/Approved/secondary_Paths.csv"
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publishers_path = f"{working_dir}/Approved/publishers.csv"
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df1 = await asyncio.to_thread(pd.read_csv, path1) if os.path.exists(path1) else pd.DataFrame()
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if df1.empty:
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logger.warning(f"File not found or empty: {path1}")
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df2 = await asyncio.to_thread(pd.read_csv, path2) if os.path.exists(path2) else pd.DataFrame()
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if df2.empty:
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logger.warning(f"File not found or empty: {path2}")
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approved_hashes = await asyncio.to_thread(pd.read_csv, hash_path) if os.path.exists(hash_path) else pd.DataFrame()
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if approved_hashes.empty:
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logger.warning(f"File not found or empty: {hash_path}")
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approved_publishers = await asyncio.to_thread(pd.read_csv, publishers_path) if os.path.exists(publishers_path) else pd.DataFrame()
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if approved_publishers.empty:
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logger.warning(f"File not found or empty: {publishers_path}")
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approved_paths = pd.concat([df1, df2], ignore_index=True) if not (df1.empty and df2.empty) else pd.DataFrame()
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dataframes = {
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"approved_paths": approved_paths,
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"approved_hashes": approved_hashes,
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"approved_publishers": approved_publishers
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}
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for name, df in dataframes.items():
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logger.debug(f"DataFrame headers for {name}: {list(df.columns)}")
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if name == "approved_paths":
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df.sort_values(by="longestcfp", inplace=True)
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elif name == "approved_hashes":
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df.sort_values(by="filename_exec", inplace=True)
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elif name == "approved_publishers":
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df.sort_values(by="publisher_hash", inplace=True)
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csv_path = f"{working_dir}/Preflight/{name}.csv"
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html_path = f"{working_dir}/Preflight/HTML/{name}.html"
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await asyncio.to_thread(df.to_csv, csv_path, index=False)
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await asyncio.to_thread(formatHTML, df, html_path)
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print("buildPreflights completed asynchronously.")
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async def splitFilepathsGrouped(df, col="filename"):
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path_task = asyncio.create_task(get_protected_value("PATH_EXCLUSION_CONST", int))
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min_files_task = asyncio.create_task(get_protected_value("MIN_FILES_FOR_PATH", int))
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path_exclusion_constant = await path_task
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min_files_for_path = await min_files_task
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def clean_split(path):
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if not isinstance(path, (str, bytes, os.PathLike)):
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return []
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parts = os.path.normpath(path).split(os.sep)
|
||||
parts = [p for p in parts if p] # Remove empty strings
|
||||
return parts
|
||||
return [p for p in parts if p]
|
||||
|
||||
# Diagnostic: log any non-string entries
|
||||
non_string_entries = df[~df[col].apply(lambda x: isinstance(x, (str, bytes, os.PathLike)))]
|
||||
if not non_string_entries.empty:
|
||||
print(f"[WARNING] Non-string entries found in column '{col}':")
|
||||
print(non_string_entries)
|
||||
logger.warning(f"Non-string entries found in column '{col}':")
|
||||
logger.debug(non_string_entries)
|
||||
|
||||
df = df.copy()
|
||||
split_paths = df[col].apply(clean_split)
|
||||
|
||||
# Filter out paths with fewer than `min_files_for_path` components
|
||||
df = df[split_paths.apply(lambda parts: len(parts) >= min_files_for_path)].copy()
|
||||
split_paths = split_paths[df.index]
|
||||
|
||||
@@ -295,11 +304,7 @@ def splitFilepathsGrouped(df, col="filename"):
|
||||
|
||||
for i, parts in enumerate(split_parts):
|
||||
filename = parts[-1]
|
||||
middle = (
|
||||
os.sep.join(parts[len(common_prefix):-1])
|
||||
if len(parts) > len(common_prefix) + 1
|
||||
else ""
|
||||
)
|
||||
middle = os.sep.join(parts[len(common_prefix):-1]) if len(parts) > len(common_prefix) + 1 else ""
|
||||
row = group_df.iloc[i].copy()
|
||||
row["longestcfp"] = prefix_str
|
||||
row["middle"] = middle
|
||||
@@ -309,19 +314,16 @@ def splitFilepathsGrouped(df, col="filename"):
|
||||
|
||||
return pd.DataFrame(new_rows).drop(columns=["group_key"])
|
||||
|
||||
def calculatePath(approved_hashes, split):
|
||||
if split:
|
||||
dfs_by_policy = [group for _, group in approved_hashes.groupby("policy")]
|
||||
else:
|
||||
dfs_by_policy = [approved_hashes]
|
||||
|
||||
badpathparts = load_env_json("BAD_PATH_PARTS", "[]")
|
||||
min_files_for_path = get_protected_value("MIN_FILES_FOR_PATH", cast_type = int)
|
||||
async def calculatePath(approved_hashes, split):
|
||||
dfs_by_policy = [group for _, group in approved_hashes.groupby("policy")] if split else [approved_hashes]
|
||||
badpathparts = await asyncio.to_thread(load_env_json, "BAD_PATH_PARTS", "[]")
|
||||
min_files_for_path = await asyncio.to_thread(get_protected_value, "MIN_FILES_FOR_PATH", int)
|
||||
|
||||
processed_dfs = []
|
||||
|
||||
for df in dfs_by_policy:
|
||||
haslcp = splitFilepathsGrouped(df, "filename_exec")
|
||||
haslcp = await splitFilepathsGrouped(df, "filename_exec")
|
||||
haslcp = haslcp.drop_duplicates()
|
||||
|
||||
forbidden = regulator(badpathparts, True)
|
||||
@@ -329,18 +331,12 @@ def calculatePath(approved_hashes, split):
|
||||
|
||||
logger.debug("Removing forbidden filepaths for path exceptions")
|
||||
print(colorText("Removing forbidden filepaths for path exceptions", "green"))
|
||||
|
||||
lcp_not_forbidden = haslcp[~forbidden_lcfp].copy()
|
||||
|
||||
lcp_not_forbidden_review = lcp_not_forbidden[
|
||||
[
|
||||
"policyname",
|
||||
"longestcfp",
|
||||
"middle",
|
||||
"filename_only",
|
||||
"file_extension",
|
||||
"sha256",
|
||||
]
|
||||
]
|
||||
lcp_not_forbidden_review = lcp_not_forbidden[[
|
||||
"policyname", "longestcfp", "middle", "filename_only", "file_extension", "sha256"
|
||||
]]
|
||||
|
||||
unique_sha_counts = (
|
||||
lcp_not_forbidden_review.groupby("longestcfp")["sha256"].nunique().reset_index()
|
||||
@@ -353,8 +349,8 @@ def calculatePath(approved_hashes, split):
|
||||
lcp_not_forbidden_review = lcp_not_forbidden_review[
|
||||
lcp_not_forbidden_review["unique_sha256_count"] >= min_files_for_path
|
||||
]
|
||||
|
||||
processed_dfs.append(lcp_not_forbidden_review)
|
||||
|
||||
pathExclusions = pd.concat(processed_dfs, ignore_index=True)
|
||||
|
||||
return pathExclusions
|
||||
return pathExclusions
|
||||
Reference in New Issue
Block a user