What it does. Analyze memory dumps using Volatility3 plugins to detect injected code, Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-plugins, or copy the skill folder into ~/.claude/skills/performing-memory-forensics-with-volatility3-plugins/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-memory-forensics-with-volatility3-plugins/SKILL.md

SKILL.md (verbatim)

name: performing-memory-forensics-with-volatility3-plugins
description: Analyze memory dumps using Volatility3 plugins to detect injected code,
  rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory
  images.
domain: cybersecurity
subdomain: malware-analysis
tags:
- memory-forensics
- volatility3
- malware-analysis
- incident-response
- process-injection
- rootkit-detection
- dfir
version: '1.0'
author: mahipal
license: Apache-2.0
d3fend_techniques:
- Executable Denylisting
- Execution Isolation
- File Metadata Consistency Validation
- Content Format Conversion
- File Content Analysis
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1027
- T1055
- T1140
- T1497
- T1003

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 volatility3 framework 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"),
                    "process": entry.get("Owner"),
                    "local": f"{entry.get('LocalAddr')}:{entry.get('LocalPort')}",
                    "remote": f"{entry.get('ForeignAddr')}:{entry.get('ForeignPort')}",
                    "state": entry.get("State"),
                    "protocol": entry.get("Proto"),
                }
                connections.append(conn)

        self.results["network_connections"] = connections
        return connections

    def extract_dlls(self, pid=None):
        """List loaded DLLs per process."""
        print(f"[+] Running windows.dlllist{f' (PID {pid})' if pid else ''}")
        args = ["--pid", str(pid)] if pid else None
        results = self.run_plugin("windows.dlllist", args)

        dlls = []
        if results:
            for entry in results:
                dlls.append({
                    "pid": entry.get("PID"),
                    "process": entry.get("Process"),
                    "base": entry.get("Base"),
                    "name": entry.get("Name"),
                    "path": entry.get("Path"),
                    "size": entry.get("Size"),
                })

        self.results["loaded_dlls"] = dlls
        return dlls

    def scan_with_yara(self, rules_path):
        """Scan memory with YARA rules."""
        print(f"[+] Running windows.yarascan with {rules_path}")
        results = self.run_plugin(
            "windows.yarascan",
            ["--yara-file", rules_path]
        )

        matches = []
        if results:
            for entry in results:
                matches.append({
                    "rule": entry.get("Rule"),
                    "pid": entry.get("PID"),
                    "process": entry.get("Process"),
                    "offset": entry.get("Offset"),
                })

        self.results["yara_matches"] = matches
        return matches

    def full_triage(self):
        """Run full malware-focused memory triage."""
        print(f"[*] Full memory triage: {self.dump_path}")
        print("=" * 60)

        self.detect_process_injection()
        self.find_hidden_processes()
        self.analyze_network()

        return self.results


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <memory_dump>")
        sys.exit(1)

    analyzer = Vol3Analyzer(sys.argv[1])
    results = analyzer.full_triage()
    print(json.dumps(results, indent=2, default=str))

Validation Criteria

• Memory dump successfully parsed with correct OS profile
• Injected processes detected via malfind with RWX regions
• Hidden processes identified through pslist/psscan comparison
• Network connections reveal C2 communication endpoints
• YARA rules match known malware signatures in memory
• Credential artifacts extracted from lsass process memory

References

• Volatility Foundation
• Volatility3 GitHub
• 2024 Volatility Plugin Contest
• Memory Forensics with Volatility 3
• MITRE ATT&CK T1055 - Process Injection

Other files in this skill

• LICENSE
• assets/template.md
• references/api-reference.md
• references/standards.md
• references/workflows.md
• scripts/agent.py
• scripts/process.py

assets/template.md (verbatim)

Memory Forensics Analysis Report

Acquisition Info

Findings Summary

Detailed Findings

Process Injection (malfind)

Network Connections

references/api-reference.md (verbatim)

API Reference — Performing Memory Forensics with Volatility3 Plugins

Libraries Used

• subprocess: Execute Volatility3 CLI with JSON output
• json: Parse Volatility3 JSON results

CLI Interface

python agent.py plugin --dump memory.raw --name pslist [--args --pid 1234]
python agent.py malproc --dump memory.raw
python agent.py inject --dump memory.raw
python agent.py network --dump memory.raw
python agent.py triage --dump memory.raw

Core Functions

run_vol3_plugin(memory_dump, plugin_name, extra_args) — Execute any Vol3 plugin

Supports 18 built-in plugins with JSON output parsing.

detect_malicious_processes(memory_dump) — Suspicious process detection

Checks pslist against 15 known attack tools (mimikatz, cobalt, rubeus, etc.). Flags cmd.exe and PowerShell execution.

detect_injected_code(memory_dump) — Code injection via malfind

Identifies memory regions with executable, non-image-backed pages.

analyze_network_connections(memory_dump) — Network artifact extraction

Extracts connections via netscan. Filters external (non-RFC1918) connections.

full_triage(memory_dump) — Combined analysis

Runs processes + injection + network analysis in single report.

Supported Volatility3 Plugins

Dependencies

pip install volatility3

references/standards.md (verbatim)

Volatility3 Memory Forensics Standards

Key Plugins for Malware Analysis

Memory Acquisition Formats

References

• Volatility3 Documentation
• Volatility Plugin Development

references/workflows.md (verbatim)

Memory Forensics Workflows

Workflow 1: Malware Triage

[Memory Dump] --> [pslist/psscan] --> [malfind] --> [dlllist] --> [netscan]
                                          |
                                          v
                                  [Dump Injected Code] --> [YARA Scan]

Workflow 2: Rootkit Detection

[Memory Dump] --> [pslist vs psscan] --> [Hidden Processes]
                                               |
                                               v
                                      [SSDT Hook Detection]
                                               |
                                               v
                                      [Inline Hook Analysis]

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