Agent skill

Extracting Credentials From Memory Dump

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.

Apache-2.0Auto-check passedDevOps & Cloud

Install Extracting Credentials From Memory Dump

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-credentials-from-memory-dump --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/extracting-credentials-from-memory-dump .claude/skills/extracting-credentials-from-memory-dump && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
extracting-credentials-from-memory-dump
GitHub stars
34k
Token cost
~3.4k tokens
SKILL.md length
466 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.

  • Works in 6 steps: Prepare Tools and Verify Memory Dump → Extract Credential Hashes with Volatility → Dump LSASS Process Memory for Detailed… → …
  • Performing memory forensics
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3 and pip

What it does

Extracting Credentials From Memory Dump is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz. Use when performing memory forensics or incident response on an LSASS or full memory dump and you need to recover credentials or Kerberos material for investigation.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in DevOps & Cloud, covering Incident response and Digital forensics. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Performing memory forensics
  • Incident response on an LSASS
  • Full memory dump and you need to recover credentials
  • Kerberos material for investigation

Example prompts

  • “Use the extracting-credentials-from-memory-dump skill to extract cached credentials, password hashes, Kerberos tickets, and authentication tokens…”
  • “/extracting-credentials-from-memory-dump”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Prepare Tools and Verify Memory Dump
  2. Extract Credential Hashes with Volatility
  3. Dump LSASS Process Memory for Detailed Analysis
  4. Extract Credentials with pypykatz
  5. Extract Kerberos Tickets and Tokens
  6. Compile Credential Findings Report

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Extracting Credentials From Memory Dump loads about 3.4k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 466 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 466 words, ~3,359 tokens.

Download SKILL.mdSave it as .claude/skills/extracting-credentials-from-memory-dump/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
extracting-credentials-from-memory-dump
description
Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz. Use when performing memory forensics or incident response on an LSASS or full memory dump and you need to recover credentials or Kerberos material for investigation.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, credential-extraction, memory-forensics, volatility, mimikatz, password-hashes, incident-response
mitre_attack
T1005, T1074, T1119, T1070, T1003
mitre_f3.version
1.1
mitre_f3.tactics
reconnaissance, positioning, initial-access
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
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

ConceptDescription
LSASS (Local Security Authority)Windows process managing authentication, storing credentials in memory
NTLM hashNT LAN Manager hash of user password used for authentication
Kerberos TGTTicket Granting Ticket allowing request of service tickets
WDigestLegacy authentication protocol storing plaintext passwords in memory (pre-Win8.1)
DPAPIData Protection API using master keys derived from user credentials
DCC2 (Domain Cached Credentials)Cached domain password hashes for offline logon
LSA SecretsEncrypted service account passwords and other secrets stored by LSA
Pass-the-HashAttack technique using extracted NTLM hashes without knowing the plaintext password

Tools & Systems

ToolPurpose
Volatility 3Memory forensics framework with hashdump, lsadump, cachedump plugins
pypykatzPython implementation of Mimikatz for cross-platform LSASS analysis
MimikatzWindows credential extraction tool (used offline against dumps)
secretsdump.pyImpacket tool for extracting secrets from SAM/SYSTEM/SECURITY
hashcatPassword hash cracking for recovered NTLM and DCC2 hashes
John the RipperAlternative password cracking tool
RubeusKerberos ticket manipulation and extraction tool
ImpacketPython toolkit for working with Windows network protocols and credentials
Show full SKILL.md (153 more words)Show less

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

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/extracting-credentials-from-memory-dump of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Questions about Extracting Credentials From Memory Dump

What does Extracting Credentials From Memory Dump do?

Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz. Extracting Credentials From Memory Dump is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts cached credentials, password hashes, Kerberos tickets, and authentication tokens from Windows memory dumps using Volatility 3, Mimikatz, and pypykatz.

When should I use Extracting Credentials From Memory Dump?

Extracting Credentials From Memory Dump fits situations like: performing memory forensics; incident response on an LSASS; full memory dump and you need to recover credentials; kerberos material for investigation.

How do I install Extracting Credentials From Memory Dump in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a claude-code`. Or copy the skill folder (skills/extracting-credentials-from-memory-dump in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/extracting-credentials-from-memory-dump in your project. Claude Code loads it when a task matches its description.

How do I install Extracting Credentials From Memory Dump in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a codex`. Or copy the skill folder (skills/extracting-credentials-from-memory-dump in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/extracting-credentials-from-memory-dump in your project. Codex loads it when a task matches its description.

Can I use Extracting Credentials From Memory Dump in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-credentials-from-memory-dump -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extracting-credentials-from-memory-dump, .gemini/skills/extracting-credentials-from-memory-dump, .github/skills/extracting-credentials-from-memory-dump and .opencode/skills/extracting-credentials-from-memory-dump in your project.

What does Extracting Credentials From Memory Dump need to run?

Going by SKILL.md and its folder, Extracting Credentials From Memory Dump needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.

Does Extracting Credentials From Memory Dump access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Extracting Credentials From Memory Dump safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Extracting Credentials From Memory Dump use?

Extracting Credentials From Memory Dump is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Extracting Credentials From Memory Dump use?

About 3.4k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 664 tokens, read only when the agent opens those files.

What are the alternatives to Extracting Credentials From Memory Dump?

Skills that share tags, products or a category with Extracting Credentials From Memory Dump: Incident Response (alirezarezvani/claude-skills, 28k stars), Responding To Incidents (trilwu/secskills, 156 stars), Incident Response (hypnguyen1209/offensive-claude, 386 stars) and Forensics Osquery (AgentSecOps/SecOpsAgentKit, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Extracting Credentials From Memory Dump?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.