Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation.
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Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation.
Step 3: Dump LSASS Process Memory for Detailed Analysis
# Dump LSASS process memory (PID from Step 1)
vol -f /cases/case-2024-001/memory/memory.raw windows.memmap --pid 684 --dump \
-o /cases/case-2024-001/analysis/lsass_dump/
# Alternative: Dump all files associated with LSASS
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles --pid 684 \
-o /cases/case-2024-001/analysis/lsass_files/
# Use procdump plugin for cleaner process dump
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles \
--pid 684 -o /cases/case-2024-001/analysis/
# Rename the dump file for pypykatz/mimikatzmv /cases/case-2024-001/analysis/lsass_dump/pid.684.dmp \
/cases/case-2024-001/analysis/lsass.dmp
Step 4: Extract Credentials with pypykatz
# Run pypykatz against the full memory dump
pypykatz lsa minidump /cases/case-2024-001/analysis/lsass.dmp \
> /cases/case-2024-001/analysis/pypykatz_results.txt 2>&1
# Run pypykatz against the raw memory dump directly
pypykatz rekall /cases/case-2024-001/memory/memory.raw \
> /cases/case-2024-001/analysis/pypykatz_full.txt 2>&1
# Parse pypykatz output for structured analysis
python3 << 'PYEOF'
import json
# pypykatz can also output JSON
import subprocess
result = subprocess.run(
['pypykatz', 'lsa', 'minidump', '/cases/case-2024-001/analysis/lsass.dmp', '-j'],
capture_output=True, text=True
)
if result.stdout:
data = json.loads(result.stdout)
print("=== EXTRACTED CREDENTIALS ===\n")
for session_key, session in data.get('logon_sessions', {}).items():
username = session.get('username', 'Unknown')
domain = session.get('domainname', '')
logon_server = session.get('logon_server', '')
logon_time = session.get('logon_time', '')
sid = session.get('sid', '')
if username and username != '(null)':
print(f"Session: {domain}\\{username}")
print(f" SID: {sid}")
print(f" Logon Server: {logon_server}")
print(f" Logon Time: {logon_time}")
# NTLM hashes
msv = session.get('msv_creds', [])
for cred in msv:
nt = cred.get('NThash', '')
lm = cred.get('LMHash', '')
if nt:
print(f" NTLM Hash: {nt}")
if lm:
print(f" LM Hash: {lm}")
# Kerberos tickets
kerb = session.get('kerberos_creds', [])
for cred in kerb:
password = cred.get('password', '')
if password:
print(f" Kerberos Password: {password}")
tickets = cred.get('tickets', [])
for ticket in tickets:
print(f" Kerberos Ticket: {ticket.get('server', '')} (type: {ticket.get('enc_type', '')})")
# WDigest (plaintext on older systems)
wdigest = session.get('wdigest_creds', [])
for cred in wdigest:
pwd = cred.get('password', '')
ifpwd:
print(f" WDigest Password: {pwd}")
# DPAPI master keys
dpapi = session.get('dpapi_creds', [])
for cred in dpapi:
mk = cred.get('masterkey', '')
if mk:
print(f" DPAPI Master Key: {mk[:40]}...")
print()
PYEOF
Step 5: Extract Kerberos Tickets and Tokens
# Extract Kerberos tickets from memory
python3 << 'PYEOF'
import subprocess, json
result = subprocess.run(
['pypykatz', 'lsa', 'minidump', '/cases/case-2024-001/analysis/lsass.dmp', '-j', '-k', '/cases/case-2024-001/analysis/kerberos/'],
capture_output=True, text=True
)
# pypykatz exports .kirbi files to the specified directory
import os
kirbi_dir = '/cases/case-2024-001/analysis/kerberos/'if os.path.exists(kirbi_dir):
for f in os.listdir(kirbi_dir):
if f.endswith('.kirbi'):
filepath = os.path.join(kirbi_dir, f)
size = os.path.getsize(filepath)
print(f" Kerberos ticket: {f} ({size} bytes)")
PYEOF
# Search process memory for authentication tokens and API keys
vol -f /cases/case-2024-001/memory/memory.raw windows.strings --pid 684 | \
grep -iE '(bearer |authorization:|api[_-]key|token=|password=|secret=)' \
> /cases/case-2024-001/analysis/auth_strings.txt
# Search for cloud credentials in memory
vol -f /cases/case-2024-001/memory/memory.raw windows.strings | \
grep -iE '(AKIA[A-Z0-9]{16}|ASIA[A-Z0-9]{16}|aws_secret_access_key)' \
> /cases/case-2024-001/analysis/aws_credentials.txt
# Search for browser session tokens
vol -f /cases/case-2024-001/memory/memory.raw windows.strings | \
grep -iE '(session_id=|PHPSESSID=|JSESSIONID=|_ga=|sid=)' \
> /cases/case-2024-001/analysis/session_tokens.txt
Step 6: Compile Credential Findings Report
# Generate credential compromise assessment
python3 << 'PYEOF'print("""
CREDENTIAL EXTRACTION REPORT
==============================
Case: 2024-001
Source: memory.raw (16 GB Windows 10 memory dump)
Analysis Date: 2024-01-20
COMPROMISED ACCOUNTS:
=====================
1. Local Accounts (SAM):
- Administrator (RID 500): NTLM hash extracted
- svcbackup (RID 1001): NTLM hash extracted
- SQLService (RID 1002): NTLM hash extracted
2. Domain Accounts (LSASS):
- CORP\\admin.user: NTLM hash + Kerberos TGT
- CORP\\svc.backup: NTLM hash + plaintext password (WDigest)
- CORP\\domain.admin: Kerberos TGS tickets for 3 services
3. Cached Domain Credentials:
- CORP\\helpdesk.user: DCC2 hash
- CORP\\it.manager: DCC2 hash
4. Cloud Credentials:
- AWS Access Key: AKIA... found in process memory (PID 3456)
- Azure AD token found in browser process memory
IMMEDIATE ACTIONS REQUIRED:
- Reset passwords for all listed accounts
- Revoke and rotate AWS access keys
- Invalidate all active Kerberos tickets (krbtgt reset)
- Review DPAPI-protected data for additional exposure
""")
PYEOF
Key Concepts
Concept
Description
LSASS (Local Security Authority)
Windows process managing authentication, storing credentials in memory
NTLM hash
NT LAN Manager hash of user password used for authentication
Kerberos TGT
Ticket Granting Ticket allowing request of service tickets
WDigest
Legacy authentication protocol storing plaintext passwords in memory (pre-Win8.1)
DPAPI
Data Protection API using master keys derived from user credentials
DCC2 (Domain Cached Credentials)
Cached domain password hashes for offline logon
LSA Secrets
Encrypted service account passwords and other secrets stored by LSA
Pass-the-Hash
Attack technique using extracted NTLM hashes without knowing the plaintext password
Tools & Systems
Tool
Purpose
Volatility 3
Memory forensics framework with hashdump, lsadump, cachedump plugins
pypykatz
Python implementation of Mimikatz for cross-platform LSASS analysis
Mimikatz
Windows credential extraction tool (used offline against dumps)
secretsdump.py
Impacket tool for extracting secrets from SAM/SYSTEM/SECURITY
hashcat
Password hash cracking for recovered NTLM and DCC2 hashes
John the Ripper
Alternative password cracking tool
Rubeus
Kerberos ticket manipulation and extraction tool
Impacket
Python toolkit for working with Windows network protocols and credentials
Common Scenarios
Scenario 1: Post-Breach Credential Assessment
Extract all cached credentials from LSASS memory to determine which accounts were exposed, prioritize password resets based on privilege level, check for golden ticket material (krbtgt hash), assess if cloud credentials were accessible.
Scenario 2: Lateral Movement Investigation
Extract NTLM hashes and Kerberos tickets to understand how the attacker moved between systems, identify pass-the-hash/pass-the-ticket artifacts, correlate extracted credentials with network logon events in event logs.
Scenario 3: Ransomware Operator Credential Theft
Analyze pre-encryption memory dump for Mimikatz execution evidence, extract all available credential types, determine if domain admin credentials were obtained, assess if krbtgt was compromised (golden ticket), plan credential rotation strategy.
Scenario 4: Cloud Credential Theft from Endpoint
Search endpoint memory for AWS access keys, Azure tokens, and GCP service account keys stored by CLI tools and browsers, identify exposed cloud permissions, immediately rotate discovered credentials, audit cloud audit logs for unauthorized access.
