/analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes,
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-memory-dumps-with-volatility --agent claude-codeHow it fires
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/analyzing-memory-dumps-with-volatility
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Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes,
SKILL.md
analyzing-memory-dumps-with-volatility.SKILL.mdname: analyzing-memory-dumps-with-volatility
description: 'Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes,
injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory
forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection
detection, or memory-resident malware investigation.
'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- memory-forensics
- Volatility
- RAM-analysis
- incident-response
mitre_attack:
- T1055
- T1003
- T1059
- T1620
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
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-i
Read more
name: analyzing-memory-dumps-with-volatility description: 'Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation. ' domain: cybersecurity subdomain: malware-analysis tags: - malware - memory-forensics - Volatility - RAM-analysis - incident-response mitre_attack: - T1055 - T1003 - T1059 - T1620 version: 1.0.0 author: mahipal license: Apache-2.0 nist_csf: - DE.AE-02 - RS.AN-03 - ID.RA-01 - DE.CM-01
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-i
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 standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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