--- name: extracting-credentials-from-memory-dump-b1ea3a283154 description: Extract cached credentials, password hashes, Kerberos tickets, and authentication tokens from memory dumps using Volatility and Mimikatz for forensic investigation. domain: cybersecurity subdomain: digital-forensics tags: - forensics - credential-extraction - memory-forensics - volatility - mimikatz - password-hashes - incident-response mitre_attack: - T1003 - T1558 - T1552 version: '1.0' author: mahipal license: Apache-2.0 nist_csf: - RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 --- # Extracting Credentials from Memory Dump ## When to Use - During incident response to determine what credentials an attacker had access to - When assessing the scope of credential compromise after a breach - For identifying accounts that need immediate password resets - When investigating lateral movement and pass-the-hash/pass-the-ticket attacks - For recovering encryption keys or authentication tokens from process memory ## Prerequisites - Memory dump in raw, ELF, or crash dump format - Volatility 3 with Windows symbol tables - Mimikatz (for offline analysis of extracted LSASS dumps) - pypykatz (Python implementation of Mimikatz for Linux-based analysis) - Understanding of Windows authentication (NTLM, Kerberos, DPAPI) - Appropriate legal authorization for credential extraction ## Workflow ### Step 1: Prepare Tools and Verify Memory Dump ```bash # Install analysis tools pip install volatility3 pypykatz # Verify memory dump integrity sha256sum /cases/case-2024-001/memory/memory.raw # Identify the OS version vol -f /cases/case-2024-001/memory/memory.raw windows.info # Verify LSASS process exists in memory vol -f /cases/case-2024-001/memory/memory.raw windows.pslist | grep -i lsass # Output: # PID PPID ImageFileName Offset(V) Threads Handles SessionId # 684 564 lsass.exe 0xffffe00123456 35 1234 0 ``` ### Step 2: Extract Credential Hashes with Volatility ```bash # Dump SAM database hashes from memory vol -f /cases/case-2024-001/memory/memory.raw windows.hashdump \ | tee /cases/case-2024-001/analysis/hashdump.txt # Output format: # User RID LM Hash NTLM Hash # Administrator 500 aad3b435b51404eeaad3b435b51404ee fc525c9683e8fe067095ba2ddc971889 # Guest 501 aad3b435b51404eeaad3b435b51404ee 31d6cfe0d16ae931b73c59d7e0c089c0 # DefaultAccount 503 aad3b435b51404eeaad3b435b51404ee 31d6cfe0d16ae931b73c59d7e0c089c0 # svcbackup 1001 aad3b435b51404eeaad3b435b51404ee 2b576acbe6bcfda7294d6bd18041b8fe # Extract LSA secrets vol -f /cases/case-2024-001/memory/memory.raw windows.lsadump \ | tee /cases/case-2024-001/analysis/lsadump.txt # Extract cached domain credentials vol -f /cases/case-2024-001/memory/memory.raw windows.cachedump \ | tee /cases/case-2024-001/analysis/cachedump.txt ``` ### Step 3: Dump LSASS Process Memory for Detailed Analysis ```bash # 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/mimikatz mv /cases/case-2024-001/analysis/lsass_dump/pid.684.dmp \ /cases/case-2024-001/analysis/lsass.dmp ``` ### Step 4: Extract Credentials with pypykatz ```bash # 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', '') if pwd: 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 ```bash # 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 ```bash # 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. ## Output Format ``` Credential Extraction Summary: Source: memory.raw (16 GB, Windows 10 Build 19041) LSASS PID: 684 Credentials Recovered: Local NTLM Hashes: 4 accounts Domain NTLM Hashes: 3 accounts Kerberos TGTs: 2 tickets Kerberos TGS: 5 service tickets Plaintext Passwords: 1 (WDigest - svc.backup) Cached Domain Creds: 2 DCC2 hashes LSA Secrets: 3 service account passwords DPAPI Master Keys: 4 keys recovered Cloud Credentials: 1 AWS access key, 1 Azure token Highest Privilege Compromised: Domain Admin (CORP\domain.admin) Recommended Actions: - Immediate: Reset all extracted account passwords - Immediate: Rotate AWS access key AKIA... - Urgent: Double krbtgt password reset (golden ticket mitigation) - High: Revoke all Kerberos tickets via krbtgt rotation - Medium: Audit DPAPI-protected data exposure ``` --- Source attribution: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/extracting-credentials-from-memory-dump/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/extracting-credentials-from-memory-dump/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Security Engineer Specialties: Backend Tools: Mimikatz, Pypykatz, Volatility Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 8b234d6556598e343527b12d02b60c68dfa08a0eab4592068945bc17dbdfefa5