What it does. Analyze volatile memory (RAM) dumps using the Volatility 3 framework Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).


Upstream	mukul975/Anthropic-Cybersecurity-Skills
Skill file	skills/performing-memory-forensics-with-volatility3/SKILL.md
License	Apache-2.0 (skill folder LICENSE)
Author	mukul975
Fetched	2026-09-10

Install

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3, or copy the skill folder into ~/.claude/skills/performing-memory-forensics-with-volatility3/.
Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-memory-forensics-with-volatility3/SKILL.md

SKILL.md (verbatim)

name: performing-memory-forensics-with-volatility3
description: Analyze volatile memory (RAM) dumps using the Volatility 3 framework
  to extract running processes, network connections, loaded modules, credentials,
  and encryption keys, and to detect process hollowing, DLL injection, or hidden
  processes/rootkits. Use during incident response on a compromised or suspect system
  when disk-based forensics alone is insufficient and volatile evidence of malware
  or intrusion must be recovered from memory.
domain: cybersecurity
subdomain: digital-forensics
tags:
- forensics
- memory-forensics
- volatility
- ram-analysis
- malware-detection
- incident-response
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- RS.AN-03
- DE.AE-02
- RS.MA-01
mitre_attack:
- T1005
- T1074
- T1119
- T1070
- T1059

Performing Memory Forensics with Volatility 3

When to Use

When analyzing a RAM dump from a compromised or suspect system
During incident response to identify running malware, injected code, or rootkits
When you need to extract credentials, encryption keys, or network connections from memory
For detecting process hollowing, DLL injection, or hidden processes
When disk-based forensics alone is insufficient and volatile data is critical

Prerequisites

Python 3.7+ installed
Volatility 3 framework installed (pip install volatility3)
Memory dump in raw, ELF, or crash dump format
Appropriate symbol tables (ISF files) for the target OS version
Sufficient disk space for analysis output (2-3x memory dump size)
Optional: YARA rules for malware scanning in memory

Workflow

Step 1: Acquire Memory Dump and Install Volatility 3

# Install Volatility 3
pip install volatility3

# Or install from source for latest features
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3
pip install -e .

# Download Windows symbol tables (ISF packs)
# Place in volatility3/symbols/ directory
wget https://downloads.volatilityfoundation.org/volatility3/symbols/windows.zip
unzip windows.zip -d /opt/volatility3/volatility3/symbols/

# Download Linux and Mac symbol packs
wget https://downloads.volatilityfoundation.org/volatility3/symbols/linux.zip
wget https://downloads.volatilityfoundation.org/volatility3/symbols/mac.zip

# Memory acquisition tools (for live systems):
# Windows: winpmem, DumpIt, FTK Imager
# Linux: LiME (Linux Memory Extractor)
sudo insmod lime-$(uname -r).ko "path=/cases/memory/linux_mem.lime format=lime"

# Verify the memory dump
file /cases/case-2024-001/memory/memory.raw
ls -lh /cases/case-2024-001/memory/memory.raw

Step 2: Identify the Operating System Profile

# Run banners plugin to identify the OS
vol -f /cases/case-2024-001/memory/memory.raw banners

# For Windows, identify the OS version
vol -f /cases/case-2024-001/memory/memory.raw windows.info

# Output example:
# Variable        Value
# Kernel Base     0xf8047e200000
# DTB             0x1ad000
# Symbols         ntkrnlmp.pdb/GUID
# Is64Bit         True
# IsPAE           False
# primary layer   Intel32e
# KdVersionBlock  0xf8047ee232c0
# Major/Minor     15.19041
# Machine Type    34404
# KeNumberProcessors 4
# SystemTime      2024-01-18 14:32:15 UTC
# NtBuildLab      19041.1.amd64fre.vb_release.191206-1406
# NtProductType   NtProductWinNt
# NtSystemRoot    C:\WINDOWS
# PE MajorOperatingSystemVersion 10
# PE MinorOperatingSystemVersion 0

# For Linux memory dumps
vol -f /cases/case-2024-001/memory/linux_mem.lime linux.info

Step 3: Enumerate Processes and Detect Anomalies

# List all running processes
vol -f /cases/case-2024-001/memory/memory.raw windows.pslist | tee /cases/case-2024-001/analysis/pslist.txt

# Show process tree (parent-child relationships)
vol -f /cases/case-2024-001/memory/memory.raw windows.pstree | tee /cases/case-2024-001/analysis/pstree.txt

# Detect hidden processes using cross-view analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.psscan | tee /cases/case-2024-001/analysis/psscan.txt

# Compare pslist vs psscan to find hidden processes
diff <(vol -f memory.raw windows.pslist | awk '{print $1}' | sort) \
     <(vol -f memory.raw windows.psscan | awk '{print $1}' | sort)

# List DLLs loaded by a suspicious process (PID 4532)
vol -f /cases/case-2024-001/memory/memory.raw windows.dlllist --pid 4532

# Check for process hollowing and injection
vol -f /cases/case-2024-001/memory/memory.raw windows.malfind | tee /cases/case-2024-001/analysis/malfind.txt

# Dump suspicious process memory for further analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.memmap --pid 4532 --dump \
   -o /cases/case-2024-001/analysis/dumps/

Step 4: Analyze Network Connections and Registry

# List active network connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | tee /cases/case-2024-001/analysis/netscan.txt

# Filter for established connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep ESTABLISHED

# Filter for listening ports
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep LISTENING

# Extract network connections with process mapping
vol -f /cases/case-2024-001/memory/memory.raw windows.netstat | tee /cases/case-2024-001/analysis/netstat.txt

# Dump registry hives from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.hivelist

# Extract specific registry keys
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.printkey \
   --key "Software\Microsoft\Windows\CurrentVersion\Run"

# Check services
vol -f /cases/case-2024-001/memory/memory.raw windows.svcscan | tee /cases/case-2024-001/analysis/services.txt

Step 5: Extract Credentials and Sensitive Data

# Dump cached credentials (hashdump)
vol -f /cases/case-2024-001/memory/memory.raw windows.hashdump | tee /cases/case-2024-001/analysis/hashes.txt

# Extract LSA secrets
vol -f /cases/case-2024-001/memory/memory.raw windows.lsadump

# Dump cached domain credentials
vol -f /cases/case-2024-001/memory/memory.raw windows.cachedump

# Search for plaintext strings in process memory
vol -f /cases/case-2024-001/memory/memory.raw windows.strings --pid 4532 \
   | grep -iE '(password|credential|token|api.key)'

# Extract command history from cmd.exe/powershell
vol -f /cases/case-2024-001/memory/memory.raw windows.cmdline | tee /cases/case-2024-001/analysis/cmdline.txt

# Extract environment variables
vol -f /cases/case-2024-001/memory/memory.raw windows.envars --pid 4532

Step 6: Scan for Malware with YARA Rules

# Scan memory with YARA rules
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/malware_index.yar | tee /cases/case-2024-001/analysis/yara_hits.txt

# Scan specific process memory
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/apt_rules.yar --pid 4532

# Check loaded kernel modules for rootkits
vol -f /cases/case-2024-001/memory/memory.raw windows.modules | tee /cases/case-2024-001/analysis/modules.txt

# Detect unlinked/hidden modules
vol -f /cases/case-2024-001/memory/memory.raw windows.modscan | tee /cases/case-2024-001/analysis/modscan.txt

# Check for SSDT hooks (System Service Descriptor Table)
vol -f /cases/case-2024-001/memory/memory.raw windows.ssdt | grep -v "ntoskrnl\|win32k"

# Dump a suspicious executable from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles --pid 4532 \
   -o /cases/case-2024-001/analysis/extracted/

Step 7: Compile Findings into a Report

# Generate comprehensive analysis summary
echo "=== MEMORY FORENSICS REPORT ===" > /cases/case-2024-001/analysis/memory_report.txt
echo "Image: memory.raw" >> /cases/case-2024-001/analysis/memory_report.txt
echo "OS: Windows 10 Build 19041" >> /cases/case-2024-001/analysis/memory_report.txt
echo "" >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Suspicious Processes ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/malfind.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Network Connections ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/netscan.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- YARA Matches ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/yara_hits.txt >> /cases/case-2024-001/analysis/memory_report.txt

# Calculate hash of the memory dump for integrity
sha256sum /cases/case-2024-001/memory/memory.raw >> /cases/case-2024-001/analysis/memory_report.txt

Key Concepts

Concept	Description
Volatile data	Information that exists only in RAM and is lost when power is removed
Process hollowing	Technique where malware replaces legitimate process memory with malicious code
DLL injection	Loading unauthorized DLLs into a running process address space
EPROCESS	Windows kernel structure representing a process; basis for process listing
Pool scanning	Searching memory for kernel object signatures to find hidden artifacts
VAD (Virtual Address Descriptor)	Memory management structure tracking process virtual memory regions
ISF (Intermediate Symbol Format)	Volatility 3 symbol table format for OS-specific structure definitions
Malfind	Plugin detecting injected code by examining VAD permissions and content

Tools & Systems

Tool	Purpose
Volatility 3	Primary open-source memory forensics framework
LiME	Linux Memory Extractor for acquiring Linux RAM dumps
WinPmem	Windows physical memory acquisition driver
DumpIt	Comae one-click Windows memory dump utility
YARA	Pattern matching engine for malware signature scanning
Rekall	Alternative memory forensics framework (Google)
MemProcFS	Memory process file system for memory analysis
strings	Extract printable strings from binary memory dumps

Common Scenarios

Scenario 1: Active Malware Investigation Acquire memory with DumpIt, run pslist/pstree to identify suspicious processes, use malfind to detect injected code in svchost.exe, dump the injected memory segment, scan with YARA rules identifying Cobalt Strike beacon, extract C2 IP from netscan, correlate with network logs.

Scenario 2: Credential Theft After Breach Run hashdump and lsadump to extract cached credentials, identify mimikatz execution in cmdline output, check for lsass.exe memory dumps in filesystem artifacts, correlate with lateral movement evidence in network connections.

Scenario 3: Rootkit Detection Compare pslist (uses EPROCESS linked list) with psscan (pool scanning) to find unlinked processes, check modules vs modscan for hidden kernel drivers, examine SSDT for hooks redirecting system calls, dump suspicious modules for static analysis.

Scenario 4: Ransomware Incident Recovery Extract encryption keys from ransomware process memory before system shutdown, identify the ransomware variant using YARA, find the initial execution point through command line artifacts, map lateral movement via network connections.

Output Format

Memory Forensics Analysis:
  Image:            memory.raw (16 GB)
  OS Identified:    Windows 10 x64 Build 19041
  Capture Time:     2024-01-18 14:32:15 UTC

  Process Analysis:
    Total Processes:    87
    Hidden Processes:   2 (PIDs: 4532, 6128)
    Injected Processes: 3 (malfind detections)
    Suspicious:         svchost.exe (PID 4532) - injected code at 0x7FFE0000

  Network Connections:
    Total:        45
    Established:  12
    Suspicious:   3 (C2 connections to 185.xx.xx.xx:443)

  Credentials Found:
    NTLM Hashes:      4 accounts
    Cached Creds:      2 domain accounts

  YARA Matches:
    CobaltStrike_Beacon:  PID 4532 (3 hits)
    Mimikatz_Memory:      PID 6128 (1 hit)

  Extracted Artifacts:   15 files dumped to /analysis/extracted/

Other files in this skill

SKILL.es.md (verbatim)

name: performing-memory-forensics-with-volatility3 description: Analyze memory dumps to extract processes, network connections, and malware artifacts using Volatility3. domain: cybersecurity subdomain: digital-forensics tags: [forensics, memory-analysis, volatility3, incident-response] version: "1.0" author: mahipal license: Apache-2.0 language: es

Análisis Forense de Memoria con Volatility3

Descripción General

Volatility3 es el framework líder de código abierto para análisis forense de memoria. Permite extraer procesos en ejecución, conexiones de red, módulos cargados, artefactos de malware, credenciales en memoria, y evidencia de actividad maliciosa desde volcados de memoria RAM de sistemas Windows, Linux y macOS.

Prerrequisitos

Python 3.8+ con Volatility3 instalado (pip install volatility3)
Volcado de memoria adquirido (formatos: raw, EWF, LiME, VMware .vmem)
Tablas de símbolos apropiadas para el SO analizado
Espacio en disco suficiente (2-3x el tamaño del volcado de memoria)

Conceptos Clave

Concepto	Descripción
Plugin PsList	Lista procesos activos con PID, PPID, tiempo de creación
Plugin NetScan	Extrae conexiones de red y puertos en escucha
Plugin MalFind	Detecta inyección de código en procesos (secciones PAGE_EXECUTE_READWRITE)
Plugin DllList	Lista DLLs cargadas por cada proceso
Plugin Handles	Muestra handles abiertos (archivos, registros, mutex)
Plugin CmdLine	Extrae líneas de comando de procesos

Pasos

Identificar el perfil del SO del volcado de memoria
Ejecutar vol -f memory.dmp windows.pslist para listar procesos
Analizar procesos sospechosos con windows.pstree para ver jerarquía
Buscar conexiones de red con windows.netscan
Detectar inyección de código con windows.malfind
Extraer artefactos específicos (DLLs, handles, líneas de comando)
Correlacionar hallazgos para construir timeline del ataque

Resultado Esperado

Reporte detallado de hallazgos forenses incluyendo procesos maliciosos identificados, conexiones C2, artefactos de malware extraídos, y timeline de actividad del atacante en el sistema comprometido.

references/api-reference.md (verbatim)

API Reference: Memory Forensics with Volatility 3

Volatility 3 CLI

Plugin	Description
`windows.info`	OS version, kernel base, system time
`windows.pslist`	List processes via EPROCESS linked list
`windows.pstree`	Process tree with parent-child relationships
`windows.psscan`	Pool scan for processes (finds hidden)
`windows.malfind`	Detect injected code in process memory
`windows.netscan`	Active network connections and listening ports
`windows.cmdline`	Command line arguments for all processes
`windows.dlllist`	DLLs loaded per process
`windows.hashdump`	Extract cached NTLM password hashes
`windows.lsadump`	LSA secrets from memory
`windows.svcscan`	Windows services enumeration
`windows.modules`	Loaded kernel modules
`windows.modscan`	Pool scan for kernel modules (finds hidden)
`windows.registry.hivelist`	List registry hives in memory
`windows.registry.printkey`	Print specific registry key values
`yarascan`	Scan memory with YARA rules
`windows.memmap`	Dump process memory to disk

Common Flags

Flag	Description
`-f <file>`	Memory dump file path
`--pid <pid>`	Filter by process ID
`--dump`	Dump matched content to files
`-o <dir>`	Output directory for dumps
`--yara-file <file>`	YARA rules file for scanning

Python Libraries

Library	Version	Purpose
`subprocess`	stdlib	Execute Volatility 3 CLI commands
`re`	stdlib	Parse plugin output

References

Volatility 3: https://github.com/volatilityfoundation/volatility3
Symbol tables: https://downloads.volatilityfoundation.org/volatility3/symbols/
LiME: https://github.com/504ensicsLabs/LiME
MemProcFS: https://github.com/ufrisk/MemProcFS

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performing-memory-forensics-with-volatility3 skill (Anthropic-Cybersecurity-Skills)