performing-memory-forensics-with-volatility3▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity.
| name | performing-memory-forensics-with-volatility3 |
| description | Analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity. |
| 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-01 - RS.AN-03 - DE.AE-02 - RS.MA-01 |
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/
How to use performing-memory-forensics-with-volatility3 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 development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-memory-forensics-with-volatility3
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-memory-forensics-with-volatility3 from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-memory-forensics-with-volatility3. Access the skill through slash commands (e.g., /performing-memory-forensics-with-volatility3) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★39 reviews- ★★★★★Shikha Mishra· Dec 20, 2024
Registry listing for performing-memory-forensics-with-volatility3 matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amelia Lopez· Dec 12, 2024
Useful defaults in performing-memory-forensics-with-volatility3 — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yuki Chen· Dec 8, 2024
We added performing-memory-forensics-with-volatility3 from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Tariq Zhang· Nov 27, 2024
performing-memory-forensics-with-volatility3 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Rahul Santra· Nov 11, 2024
Keeps context tight: performing-memory-forensics-with-volatility3 is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ira Li· Nov 3, 2024
I recommend performing-memory-forensics-with-volatility3 for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Johnson· Oct 22, 2024
Keeps context tight: performing-memory-forensics-with-volatility3 is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Kofi Khanna· Oct 18, 2024
performing-memory-forensics-with-volatility3 has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Pratham Ware· Oct 2, 2024
I recommend performing-memory-forensics-with-volatility3 for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ishan Liu· Sep 25, 2024
Solid pick for teams standardizing on skills: performing-memory-forensics-with-volatility3 is focused, and the summary matches what you get after install.
showing 1-10 of 39