mysticmomba/AirlockTools
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 3 Wiki Activity

New Master candidate

Browse Source
This commit is contained in:
=
2025-08-25 12:35:27 -04:00
parent b187d8cd79
commit 5f06e60fdd
5 changed files with 60 additions and 125 deletions
Download Patch File Download Diff File Expand all files Collapse all files
utils/allowlist.py
+1 -69
View File
@@ -19,7 +19,6 @@ import os
import time
import utils.colortext as ct
def pullPolicyExechistories(url, choice, policiesnames, outputjson: bool):
headers = {
@@ -74,71 +73,4 @@ def listPolicies(url):
policyids.append(list['groupid'])
choice = input(ct.colorText("Select Policy Group: ", "white"))
choice = int(choice) - 1
checkpoint = '000000000000000000000000'
json_output = {'error': 'Success', 'response': {'exechistories': []}}
while True:
json_response_data = checkpoint_stomper(checkpoint, url, policiesnames[choice], headers)
if not json_response_data['response']['exechistories']:
break
for index, item in enumerate(json_response_data['response']['exechistories']):
if index == len(json_response_data['response']['exechistories']) -1:
checkpoint = item['checkpoint']
print(f"Date Greater than 30 Days, Stepping to new Checkpoint. {item['checkpoint']}")
else:
if (datetime.date.today() - datetime.timedelta(days=10) > datetime.datetime.strptime(item['datetime'].replace(' +0000 UTC', ''), '%Y-%m-%dT%H:%M:%SZ').date()):
pass
else:
#json_output['response']['exechistories'].append(json_response_data['response']['exechistories'][1])
for output in json_response_data['response']['exechistories']:
json_output['response']['exechistories'].append(output)
json_output = json.dumps(json_output)
if outputjson == True:
return json_output
#endpoint = url + '/v1/logging/exechistories'
#payload_dict = {
# "type":[1, 2, 6, 7],
# "checkpoint":"000000000000000000000000",
# "policy": [policiesnames[choice]]
#}
#payload = json.dumps(payload_dict)
#print(payload)
#response = requests.request("POST", endpoint, headers=headers, data=payload, verify=False)
#parse_text = json.loads(response.text)
#text_response = checkpoint_stomper(parse_text['response']['exechistories'], url, policiesnames[choice])
#
#if outputjson == False:
# return response
#
#parse_text = json.loads(response.text)
#
#for item in parse_text['response']['exechistories']:
# print(item['checkpoint'])
# print(item['datetime'])
# print(item['hostname'])
# print(item['filename'])
# checkpoint_stomper(item['checkpoint'], endpoint, headers, policiesnames[choice])
def checkpoint_stomper(checkpoint, url, policy, headers):
endpoint = url + '/v1/logging/exechistories'
payload_dict = {
"type":[1,2,6,7],
"checkpoint": checkpoint,
"policy": [policy]
}
payload = json.dumps(payload_dict)
response = requests.request("POST", endpoint, headers=headers, data=payload, verify=False)
parse_text = json.loads(response.text)
return parse_text
#for index, item in enumerate(parse_text):
# if index == len(parse_text) - 1:
# checkpoint = item['checkpoint']
# print(f"Time: {item['datetime']} Checkpoint: {item['checkpoint']}")
# response_fuzzer(checkpoint, url, policyname)
# else:
# if (datetime.date.today() - datetime.timedelta(days=30) > datetime.datetime.strptime(item['datetime'].replace( ' +0000 UTC', ''), '%Y-%m-%dT%H:%M:%SZ').date()):
# pass
# else:
# response_fuzzer(checkpoint, url, policyname)
print("Finished")
return choice, policiesnames
utils/hashfunctions.py
+23 -13
View File
@@ -90,28 +90,38 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
df = aug_df.copy()
def reputationtool(row, threat_tolerance):
if row["reputation_scannermatch"] == "N/A":
return True
def reputationtool(row):
val = row["reputation_scannermatch"]
if pd.isna(val) or val == "N/A":
return row["publisher_y"] == "Not Signed"
try:
return int(val) > threat_tolerance
except (ValueError, TypeError):
return row["publisher_y"] == "Not Signed"
mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
df["reputation_flag"] = df.apply(reputationtool, axis=1)
mask_approved = (
((df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))) &
(df["publisher_y"] != "Not Signed") # explicitly signed
) | (
(df["publisher_y"] == "Not Signed") &
(~df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)) &
(~df["publisher_y"].isin(untrusted_publishers)) # exclude untrusted even if unsigned
mask_needsreview = (
((df["publisher_y"] == "Not Signed") & df["reputation_flag"]) |
(df["reputation_status"] == "UNKNOWN")
)
mask_approved = (
(
(df["publisher_y"] != "Not Signed") &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
) |
(
(df["publisher_y"] == "Not Signed") &
~df["reputation_flag"] &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
)
)
needsreview_df = df[mask_needsreview]
approved_df = df[mask_approved]
remaining_df = df[~(mask_needsreview | mask_approved)]
unapproved_df = df[~(mask_needsreview | mask_approved)]
return needsreview_df, approved_df, remaining_df
return needsreview_df, approved_df, unapproved_df
utils/pathfunctions.py
+3 -27
View File
@@ -57,45 +57,21 @@ def filepathInitialGroup(df: pd.DataFrame):
break
return join_parts(prefix)
# Step 6: Group directories by shared prefix using custom logic
"""
Loop through each directory path
directories: list of all directory paths.
groups: will hold lists of grouped directories.
used: tracks which directories have already been grouped.
"""
# Step 6: Group directories by shared prefix
directories = df["directory"].tolist()
groups = []
used = set()
#For Each directory, compare it with others
"""
Skip if already grouped.
Start a new group with the current path.
parts_i is the list of folder names in the path (e.g., ["C:", "Users", "John", "Documents"]).
"""
for i, path in enumerate(directories):
if path in used:
continue
group = [path]
parts_i = get_parts(path)
#Compare with all other directories: For each other directory, split it into parts and find the common prefix (shared folder structure).
"""
Logic:
If the directory is deep (>3 parts) and shares at least 3 parts → group it.
If it's exactly 3 parts long and shares at least 2 → group it.
Or, if it shares all but one part and is deep → group it.
These rules are designed to:
Group directories that are closely related in structure.
Avoid grouping unrelated paths that just happen to start similarly.
"""
for j in range(i + 1, len(directories)):
parts_j = get_parts(directories[j])
common = os.path.commonprefix([parts_i, parts_j])
#Apply grouping rules
if (len(parts_i) > 3 and len(common) >= 3) or (len(parts_i) == 3 and len(common) >= 2):
group.append(directories[j])
used.add(directories[j])
@@ -123,7 +99,7 @@ def filepathInitialGroup(df: pd.DataFrame):
path_eligible = grouped_df[grouped_df["depth"] > 2].drop(columns=["depth"])
path_ineligible = grouped_df[grouped_df["depth"] <= 2].drop(columns=["depth"])
# Step 10: Move entries from eligible to ineligible if grouped_directory contains 'C:\Users' or 'c$\Users'
# Step 10: Move entries from eligible to ineligible if grouped_directory contains excluded directories
mask = path_eligible["grouped_directory"].str.contains(r"(?i)(?:\\Users|\\c\$\\Users|inetpub\\wwwroot|windows\\temp)", na=False)
move_to_ineligible = path_eligible[mask]
path_eligible = path_eligible[~mask]