external/anthropic-cybersecurity-skills/skills/performing-cloud-storage-forensic-acquisition/SKILL.md
Perform forensic acquisition of cloud storage services including Google Drive, OneDrive, Dropbox, and Box by pulling API-based remote data such as revision history and audit logs, and collecting local sync-client artifacts including KAPE targets and OneDrive databases from endpoints. Use during incident response or e-discovery when evidence resides in cloud-synced storage and both cloud-side and endpoint-side artifacts must be preserved.
npx skillsauth add seikaikyo/dash-skills performing-cloud-storage-forensic-acquisitionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Cloud storage forensic acquisition involves collecting digital evidence from services like Google Drive, OneDrive, Dropbox, and Box through both API-based remote acquisition and local endpoint artifact analysis. Modern investigations must address the challenge that cloud-synced files may exist in multiple states: locally synchronized, cloud-only (on-demand), cached, and deleted. Endpoint devices that have synchronized with cloud storage contain a wealth of metadata about locally synced files, files present only in the cloud, and even deleted items recoverable from cache folders. API-based acquisition using service-specific APIs provides direct access to remote data with valid credentials and proper legal authorization.
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
import io
import os
import json
from datetime import datetime
class GoogleDriveForensicAcquisition:
"""Forensically acquire files and metadata from Google Drive via API."""
def __init__(self, credentials_path: str, output_dir: str):
self.creds = Credentials.from_authorized_user_file(credentials_path)
self.service = build("drive", "v3", credentials=self.creds)
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
self.acquisition_log = []
def list_all_files(self, include_trashed: bool = True) -> list:
"""List all files including trashed items."""
files = []
page_token = None
query = "" if include_trashed else "trashed = false"
while True:
results = self.service.files().list(
q=query,
pageSize=1000,
fields="nextPageToken, files(id, name, mimeType, size, "
"createdTime, modifiedTime, trashed, trashedTime, "
"owners, sharingUser, permissions, md5Checksum, "
"parents, webViewLink, driveId)",
pageToken=page_token
).execute()
files.extend(results.get("files", []))
page_token = results.get("nextPageToken")
if not page_token:
break
return files
def download_file(self, file_id: str, file_name: str, mime_type: str) -> str:
"""Download a file from Google Drive preserving forensic integrity."""
output_path = os.path.join(self.output_dir, file_name)
if mime_type.startswith("application/vnd.google-apps"):
export_formats = {
"application/vnd.google-apps.document": "application/pdf",
"application/vnd.google-apps.spreadsheet": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"application/vnd.google-apps.presentation": "application/pdf",
}
export_mime = export_formats.get(mime_type, "application/pdf")
request = self.service.files().export_media(fileId=file_id, mimeType=export_mime)
else:
request = self.service.files().get_media(fileId=file_id)
with io.FileIO(output_path, "wb") as fh:
downloader = MediaIoBaseDownload(fh, request)
done = False
while not done:
_, done = downloader.next_chunk()
self.acquisition_log.append({
"timestamp": datetime.utcnow().isoformat(),
"file_id": file_id,
"file_name": file_name,
"output_path": output_path,
"action": "downloaded"
})
return output_path
def get_activity_log(self, file_id: str) -> list:
"""Retrieve activity/revision history for a specific file."""
revisions = self.service.revisions().list(
fileId=file_id,
fields="revisions(id, modifiedTime, lastModifyingUser, size, md5Checksum)"
).execute()
return revisions.get("revisions", [])
def export_acquisition_report(self) -> str:
"""Export acquisition log for chain of custody documentation."""
report_path = os.path.join(self.output_dir, "acquisition_log.json")
with open(report_path, "w") as f:
json.dump({
"acquisition_start": self.acquisition_log[0]["timestamp"] if self.acquisition_log else None,
"acquisition_end": datetime.utcnow().isoformat(),
"total_files": len(self.acquisition_log),
"entries": self.acquisition_log
}, f, indent=2)
return report_path
import msal
import requests
import os
import json
from datetime import datetime
class OneDriveForensicAcquisition:
"""Forensically acquire files and metadata from OneDrive via Microsoft Graph API."""
def __init__(self, client_id: str, tenant_id: str, client_secret: str, output_dir: str):
self.output_dir = output_dir
os.makedirs(output_dir, exist_ok=True)
authority = f"https://login.microsoftonline.com/{tenant_id}"
self.app = msal.ConfidentialClientApplication(
client_id, authority=authority, client_credential=client_secret
)
token_result = self.app.acquire_token_for_client(
scopes=["https://graph.microsoft.com/.default"]
)
self.access_token = token_result.get("access_token")
self.headers = {"Authorization": f"Bearer {self.access_token}"}
self.base_url = "https://graph.microsoft.com/v1.0"
def list_user_files(self, user_id: str) -> list:
"""List all files in user's OneDrive."""
url = f"{self.base_url}/users/{user_id}/drive/root/children"
files = []
while url:
response = requests.get(url, headers=self.headers)
data = response.json()
files.extend(data.get("value", []))
url = data.get("@odata.nextLink")
return files
def download_file(self, user_id: str, item_id: str, filename: str) -> str:
"""Download a file from OneDrive."""
url = f"{self.base_url}/users/{user_id}/drive/items/{item_id}/content"
response = requests.get(url, headers=self.headers, stream=True)
output_path = os.path.join(self.output_dir, filename)
with open(output_path, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
return output_path
def get_deleted_items(self, user_id: str) -> list:
"""Retrieve items from OneDrive recycle bin."""
url = f"{self.base_url}/users/{user_id}/drive/special/recyclebin/children"
response = requests.get(url, headers=self.headers)
return response.json().get("value", [])
# Collect all cloud storage artifacts using KAPE
kape.exe --tsource C: --tdest C:\Output\CloudArtifacts --target GoogleDrive,OneDrive,Dropbox,Box
# OneDrive artifacts
# %USERPROFILE%\AppData\Local\Microsoft\OneDrive\logs\
# %USERPROFILE%\AppData\Local\Microsoft\OneDrive\settings\
# %USERPROFILE%\OneDrive\
# Google Drive artifacts
# %USERPROFILE%\AppData\Local\Google\DriveFS\
# Contains metadata SQLite databases and cached files
# Dropbox artifacts
# %USERPROFILE%\AppData\Local\Dropbox\
# %USERPROFILE%\Dropbox\.dropbox.cache\
# Contains filecache.dbx (encrypted SQLite), host.dbx, config.dbx
import sqlite3
import os
def analyze_onedrive_sync_engine(db_path: str) -> list:
"""Analyze OneDrive SyncEngineDatabase for file metadata."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Query for all tracked files including cloud-only items
cursor.execute("""
SELECT fileName, fileSize, lastChange,
resourceID, parentResourceID, eTag
FROM od_ClientFile_Records
ORDER BY lastChange DESC
""")
files = []
for row in cursor.fetchall():
files.append({
"filename": row[0],
"size": row[1],
"last_change": row[2],
"resource_id": row[3],
"parent_id": row[4],
"etag": row[5]
})
conn.close()
return files
| Service | Local Database | Cache Location | Log Files | |---------|---------------|----------------|-----------| | OneDrive | SyncEngineDatabase.db | %LOCALAPPDATA%\Microsoft\OneDrive\cache\ | %LOCALAPPDATA%\Microsoft\OneDrive\logs\ | | Google Drive | metadata_sqlite_db | %LOCALAPPDATA%\Google\DriveFS{account}\content_cache\ | %LOCALAPPDATA%\Google\DriveFS\Logs\ | | Dropbox | filecache.dbx (encrypted) | %APPDATA%\Dropbox.dropbox.cache\ | %APPDATA%\Dropbox\logs\ | | Box | sync_db | %LOCALAPPDATA%\Box\Box\cache\ | %LOCALAPPDATA%\Box\Box\logs\ |
$ python3 cloud_forensic_acquire.py --provider google-drive --auth /tokens/gdrive_token.json \
--user [email protected] --output /acquisition/gdrive
Cloud Storage Forensic Acquisition Tool v3.2
==============================================
Provider: Google Drive
Account: [email protected]
Start Time: 2024-01-19 08:00:15 UTC
Auth Method: Admin SDK (domain-wide delegation)
[+] Enumerating files...
Total files: 2,345
Total folders: 178
Shared with me: 456
Trashed items: 89 (included in acquisition)
Total size: 14.7 GB
[+] Acquiring file contents...
Downloaded: 2,345 / 2,345 [████████████████████████████████] 100%
Errors: 0
Elapsed: 18m 32s
[+] Acquiring metadata...
File metadata: 2,345 entries
Revision history: 8,912 revisions across 1,234 files
Sharing permissions: 3,456 permission entries
Activity log: 12,345 events
[+] Acquiring trashed items...
Recovered: 89 / 89 items (234 MB)
--- Acquisition Log ---
Timestamp (UTC) | Action | File | Size | SHA-256
2024-01-19 08:00:45 | Downloaded | /My Drive/Finance/Q4_Report.xlsm | 245 KB | 7a3b8c9d...
2024-01-19 08:00:46 | Downloaded | /My Drive/Finance/Budget_2024.xlsx | 1.2 MB | 8b4c9d0e...
...
2024-01-19 08:02:12 | Trash-Recovered | /Trash/employee_list_full.csv | 4.5 MB | 9c5d0e1f...
2024-01-19 08:02:13 | Trash-Recovered | /Trash/network_diagram_v3.vsdx | 2.1 MB | 0d6e1f2a...
2024-01-19 08:02:14 | Trash-Recovered | /Trash/credentials_backup.kdbx | 128 KB | 1e7f2a3b...
--- Sharing Analysis ---
Files Shared Externally:
/My Drive/Finance/Q4_Report.xlsm → [email protected] (2024-01-16 03:10 UTC)
/My Drive/HR/employee_list_full.csv → [email protected] (2024-01-16 03:12 UTC)
/My Drive/IT/network_diagram_v3.vsdx → anonymous (link sharing, 2024-01-16 03:15 UTC)
--- Revision History (Suspicious) ---
File: /My Drive/Finance/Q4_Report.xlsm
Rev 1: 2024-01-10 09:00:00 UTC (245 KB) - Original
Rev 2: 2024-01-15 14:35:00 UTC (248 KB) - Modified (macro added)
Rev 3: 2024-01-16 03:05:00 UTC (245 KB) - Reverted (macro removed - anti-forensics)
Acquisition Summary:
Files acquired: 2,345 (14.7 GB)
Trashed items: 89 (234 MB)
Revisions: 8,912
Chain of custody hash (full archive):
SHA-256: a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2
Output directory: /acquisition/gdrive/
Acquisition log: /acquisition/gdrive/acquisition_log.csv
Completion Time: 2024-01-19 08:18:47 UTC
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.