performing-memory-forensics-with-volatility3-plugins
Helps your agent analyze computer memory to find malicious code and information theft. It's useful for investigating computer attacks.
What it does
This tool analyzes computer memory copies (called 'memory dumps') to find suspicious activity. It looks for hidden programs, malicious code trying to disguise itself, and information that might have been stolen.
When to use it
- ✓When you suspect a computer has been hacked.
- ✓When investigating security incidents.
- ✓To test if the computer's defenses are working.
Example of what to ask your AI
“Analyze this memory dump for malware.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses a program called Volatility3 to examine RAM data. It runs specific checks to identify malicious behavior patterns, such as injected code or hidden processes.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-pluginsOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/performing-memory-forensics-with-volatility3-plugins/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Performing Memory Forensics with Volatility3 Plugins
Overview
Volatility3 (v2.26.0+, feature parity release May 2025) is the standard framework for memory forensics, replacing the deprecated Volatility2. It analyzes RAM dumps from Windows, Linux, and macOS to detect malicious processes, code injection, rootkits, credential harvesting, and network connections that disk-based forensics cannot reveal. Key plugins include windows.malfind (detecting RWX memory regions indicating injection), windows.psscan (finding hidden processes), windows.dlllist (enumerating loaded modules), windows.netscan (active network connections), and windows.handles (open file/registry handles). The 2024 Plugin Contest introduced ETW Scan for extracting Event Tracing for Windows data from memory.
When to Use
- When conducting security assessments that involve performing memory forensics with volatility3 plugins
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- Python 3.9+ with
volatility3framework installed - Memory dump files (
.raw,.dmp,.vmem,.lime) - Windows symbol tables (ISF files, auto-downloaded)
- Understanding of Windows process memory architecture
- YARA integration for in-memory pattern scanning
Workflow
Step 1: Process Analysis for Malware Detection
#!/usr/bin/env python3
"""Volatility3-based memory forensics automation for malware analysis."""
import subprocess
import json
import sys
import os
class Vol3Analyzer:
"""Automate Volatility3 plugin execution for malware analysis."""
def __init__(self, dump_path, vol3_path="vol"):
self.dump_path = dump_path
self.vol3 = vol3_path
self.results = {}
def run_plugin(self, plugin, extra_args=None):
"""Execute a Volatility3 plugin and capture output."""
cmd = [
self.vol3, "-f", self.dump_path,
"-r", "json", plugin,
]
if extra_args:
cmd.extend(extra_args)
try:
result = subprocess.run(
cmd, capture_output=True, text=True, timeout=300
)
if result.returncode == 0:
return json.loads(result.stdout)
except (subprocess.TimeoutExpired, json.JSONDecodeError) as e:
print(f" [!] {plugin} failed: {e}")
return None
def detect_process_injection(self):
"""Use malfind to detect injected code regions."""
print("[+] Running windows.malfind (code injection detection)")
results = self.run_plugin("windows.malfind")
injected = []
if results:
for entry in results:
injected.append({
"pid": entry.get("PID"),
"process": entry.get("Process"),
"address": entry.get("Start VPN"),
"protection": entry.get("Protection"),
"hexdump": entry.get("Hexdump", "")[:200],
})
print(f" [!] Injection in PID {entry.get('PID')} "
f"({entry.get('Process')}) at {entry.get('Start VPN')}")
self.results["injected_processes"] = injected
return injected
def find_hidden_processes(self):
"""Compare pslist vs psscan to find hidden processes."""
print("[+] Running process comparison (pslist vs psscan)")
pslist = self.run_plugin("windows.pslist")
psscan = self.run_plugin("windows.psscan")
if not pslist or not psscan:
return []
list_pids = {e.get("PID") for e in pslist}
scan_pids = {e.get("PID") for e in psscan}
hidden = scan_pids - list_pids
if hidden:
print(f" [!] {len(hidden)} hidden processes found!")
for entry in psscan:
if entry.get("PID") in hidden:
print(f" PID {entry['PID']}: {entry.get('ImageFileName')}")
self.results["hidden_processes"] = list(hidden)
return list(hidden)
def analyze_network(self):
"""Extract active network connections."""
print("[+] Running windows.netscan")
results = self.run_plugin("windows.netscan")
connections = []
if results:
for entry in results:
conn = {
"pid": entry.get("PID"),
…
FAQ
What is a 'memory dump'?
It's an exact copy of everything that was in the computer's RAM at a specific moment. It's like taking a 'snapshot' of the memory.
What operating systems are supported?
It works with memory from Windows, Linux, and macOS computers.
Do I need to know how to program to use this?
Ideally, some technical knowledge is helpful, as it involves the command line and understanding how computers work internally, but the tool automates many complex parts.
Details
Original description by the author
Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.
Popularity
- GitHub stars
- 35k
- Ranking position
- #6101
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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