GCP GCS Bucket File List
from kittysploit import *
import json
import xml.etree.ElementTree as ET
from urllib.parse import quote, urlparse
from lib.protocols.http.http_client import Http_client
class Module(Auxiliary, Http_client):
__info__ = {
"name": "GCP GCS Bucket File List",
"author": ["KittySploit Team"],
"description": "List files from a Google Cloud Storage bucket using anonymous access.",
"tags": ["gcp", "gcs", "cloud", "enumeration"],
}
target = OptString("", "GCS bucket name or URL (e.g. my-bucket or https://storage.googleapis.com/my-bucket)", required=True)
prefix = OptString("", "Optional object key prefix", required=False)
max_results = OptString("0", "Maximum objects to return (0 or 'all' = unlimited)", required=False)
display_mode = OptString("batch", "Display mode: stream|batch|end", required=False)
display_batch_size = OptString("100", "Batch size for display_mode=batch", required=False)
progress_every_pages = OptString("1", "Show progress every N pages", required=False)
timeout = OptString("10", "HTTP timeout in seconds", required=False)
output_file = OptString("", "Optional JSON output file", required=False)
def _to_int(self, value, default_value):
try:
return max(1, int(str(value).strip()))
except Exception:
return default_value
def _parse_max_results(self, value):
raw = str(value).strip().lower()
if raw in ("0", "all", "unlimited", "none", ""):
return 0
try:
return max(1, int(raw))
except Exception:
return 0
def _normalize_bucket(self, value):
raw = str(value).strip()
if not raw:
return ""
if raw.startswith(("http://", "https://")):
try:
parsed = urlparse(raw)
host = parsed.hostname or ""
if host == "storage.googleapis.com":
path_parts = [p for p in (parsed.path or "").split("/") if p]
return path_parts[0] if path_parts else ""
if host.endswith(".storage.googleapis.com"):
return host.replace(".storage.googleapis.com", "")
return host.split(".")[0]
except Exception:
return ""
return raw
def _http_get_url(self, url, timeout_seconds):
parsed = urlparse(url)
host = parsed.hostname
if not host:
return None
scheme = (parsed.scheme or "https").lower()
port = parsed.port or (443 if scheme == "https" else 80)
path = parsed.path or "/"
if parsed.query:
path = f"{path}?{parsed.query}"
old_target = self.target
old_port = getattr(self, "port", 443)
old_ssl = getattr(self, "ssl", True)
try:
self.target = host
self.port = int(port)
self.ssl = (scheme == "https")
return self.http_request(
method="GET",
path=path,
allow_redirects=True,
timeout=timeout_seconds,
)
except Exception:
return None
finally:
self.target = old_target
self.port = old_port
self.ssl = old_ssl
def _parse_list_xml(self, xml_text):
objects = []
next_token = ""
if not xml_text:
return objects, next_token
def _strip_tag(tag):
return tag.split("}", 1)[1] if "}" in tag else tag
try:
root = ET.fromstring(xml_text)
for elem in root.iter():
if _strip_tag(elem.tag) == "Contents":
key = ""
size = 0
last_modified = ""
etag = ""
storage_class = ""
for child in elem:
name = _strip_tag(child.tag)
value = (child.text or "").strip()
if name == "Key":
key = value
elif name == "Size":
try:
size = int(value or "0")
except Exception:
size = 0
elif name == "LastModified":
last_modified = value
elif name == "ETag":
etag = value.strip('"')
elif name == "StorageClass":
storage_class = value
if key:
objects.append({
"key": key,
"size": size,
"last_modified": last_modified,
"etag": etag,
"storage_class": storage_class,
})
for elem in root.iter():
name = _strip_tag(elem.tag)
if name in ("NextContinuationToken", "NextMarker"):
next_token = (elem.text or "").strip()
if next_token:
break
except Exception:
return [], ""
return objects, next_token
def run(self):
bucket = self._normalize_bucket(self.target)
if not bucket:
print_error("target must be a GCS bucket name or URL")
return {"error": "invalid target"}
timeout_seconds = self._to_int(self.timeout, 10)
max_items = self._parse_max_results(self.max_results)
display_mode = str(self.display_mode).strip().lower() or "batch"
if display_mode not in ("stream", "batch", "end"):
display_mode = "batch"
display_batch_size = self._to_int(self.display_batch_size, 100)
progress_every_pages = self._to_int(self.progress_every_pages, 1)
prefix = str(self.prefix).strip()
print_info(f"Listing GCS objects from {bucket}")
continuation = ""
collected = []
errors = []
page_count = 0
displayed_count = 0
while True:
if max_items > 0:
remaining = max_items - len(collected)
if remaining <= 0:
break
page_size = min(1000, max(1, remaining))
else:
page_size = 1000
query = f"list-type=2&max-keys={page_size}"
if prefix:
query += f"&prefix={quote(prefix)}"
if continuation:
query += f"&continuation-token={quote(continuation)}"
url = f"https://storage.googleapis.com/{bucket}?{query}"
resp = self._http_get_url(url, timeout_seconds)
if not resp:
errors.append("request_failed")
break
if resp.status_code not in (200,):
errors.append(f"http_{resp.status_code}")
if resp.status_code in (401, 403):
print_error("Access denied. Bucket may be private.")
elif resp.status_code == 404:
print_error("Bucket not found.")
else:
print_error(f"Unexpected status code: {resp.status_code}")
break
items, next_token = self._parse_list_xml(resp.text or "")
if not items and not next_token:
break
collected.extend(items)
page_count += 1
if progress_every_pages > 0 and page_count % progress_every_pages == 0:
print_status(
f"Listing progress: pages={page_count} discovered={len(collected)} "
f"mode={display_mode}"
)
if display_mode == "stream":
for item in items:
print_info(f" {item.get('key')} | {item.get('size')} bytes | {item.get('storage_class')}")
displayed_count += 1
elif display_mode == "batch":
while displayed_count + display_batch_size <= len(collected):
batch = collected[displayed_count:displayed_count + display_batch_size]
print_status(
f"Displaying batch {int(displayed_count / max(1, display_batch_size)) + 1} "
f"({displayed_count + 1}-{displayed_count + len(batch)})"
)
for item in batch:
print_info(f" {item.get('key')} | {item.get('size')} bytes | {item.get('storage_class')}")
displayed_count += len(batch)
continuation = next_token
if not continuation:
break
if max_items > 0:
collected = collected[:max_items]
total_bytes = sum(int(x.get("size", 0) or 0) for x in collected)
result = {
"target": f"storage.googleapis.com/{bucket}",
"provider": "gcp_gcs",
"bucket": bucket,
"count": len(collected),
"total_bytes": total_bytes,
"prefix": prefix,
"errors": errors,
"files": collected,
}
if collected:
print_success(f"Found {len(collected)} object(s), total={total_bytes} bytes")
if display_mode == "end":
for item in collected:
print_info(f" {item.get('key')} | {item.get('size')} bytes | {item.get('storage_class')}")
elif display_mode == "batch" and displayed_count < len(collected):
batch = collected[displayed_count:]
print_status(
f"Displaying final batch ({displayed_count + 1}-{displayed_count + len(batch)})"
)
for item in batch:
print_info(f" {item.get('key')} | {item.get('size')} bytes | {item.get('storage_class')}")
else:
print_warning("No object listed (or inaccessible bucket).")
if self.output_file:
try:
with open(str(self.output_file), "w") as fp:
json.dump(result, fp, indent=2)
print_success(f"Results saved to {self.output_file}")
except Exception as e:
print_error(f"Failed to save output: {e}")
return result
def get_graph_nodes(self, data):
if not isinstance(data, dict) or "error" in data:
return [], []
root = data.get("target", "gcs")
nodes = [{
"id": root,
"label": root,
"group": "hostname",
"icon": "🟥",
"custom_info": (
f"Bucket: {data.get('bucket', 'n/a')}\n"
f"Files: {data.get('count', 0)}\n"
f"Total bytes: {data.get('total_bytes', 0)}"
),
}]
edges = []
for i, f in enumerate(data.get("files", [])[:40]):
nid = f"gcs_{i}_{data.get('bucket', 'b')}"
nodes.append({
"id": nid,
"label": f.get("key", "object")[:90],
"group": "generic",
"icon": "📄",
"custom_info": (
f"Key: {f.get('key', 'n/a')}\n"
f"Size: {f.get('size', 0)} bytes\n"
f"Storage class: {f.get('storage_class', '')}\n"
f"Last modified: {f.get('last_modified', '')}"
),
})
edges.append({
"from": root,
"to": nid,
"label": "object",
"custom_info": "GCS object in bucket",
})
return nodes, edges
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02 May 2026 21:27Current
5.8Medium risk
Vulners AI Score5.8