Agent skill

Performing Memory Forensics With Volatility3 Plugins

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

Apache-2.0Auto-check passedSecurity

Install Performing Memory Forensics With Volatility3 Plugins

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-plugins -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-memory-forensics-with-volatility3-plugins --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-memory-forensics-with-volatility3-plugins .claude/skills/performing-memory-forensics-with-volatility3-plugins && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-memory-forensics-with-volatility3-plugins
GitHub stars
34k
Token cost
~2.1k tokens
SKILL.md length
237 words
Files
8 (incl. scripts, references, assets)
Skills in repo
639
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

  • Tasks that involve Digital forensics
  • SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Performing Memory Forensics With Volatility3 Plugins is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in Security, covering Digital forensics. It works with Linux and macOS. The repository describes itself as: 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…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Digital forensics

Example prompts

  • “/performing-memory-forensics-with-volatility3-plugins”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • volatilityfoundation.org
    • github.com
    • newtonpaul.com
    • attack.mitre.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Memory Forensics With Volatility3 Plugins loads about 2.1k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 237 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 237 words, ~2,133 tokens.

Download SKILL.mdSave it as .claude/skills/performing-memory-forensics-with-volatility3-plugins/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
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
python
#!/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

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/performing-memory-forensics-with-volatility3-plugins of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Works with

Categories

Questions about Performing Memory Forensics With Volatility3 Plugins

What does Performing Memory Forensics With Volatility3 Plugins do?

Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images. Performing Memory Forensics With Volatility3 Plugins is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.

When should I use Performing Memory Forensics With Volatility3 Plugins?

Performing Memory Forensics With Volatility3 Plugins fits situations like: tasks that involve Digital forensics.

How do I install Performing Memory Forensics With Volatility3 Plugins in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-plugins -a claude-code`. Or copy the skill folder (skills/performing-memory-forensics-with-volatility3-plugins in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-memory-forensics-with-volatility3-plugins in your project. Claude Code loads it when a task matches its description.

How do I install Performing Memory Forensics With Volatility3 Plugins in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-plugins -a codex`. Or copy the skill folder (skills/performing-memory-forensics-with-volatility3-plugins in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-memory-forensics-with-volatility3-plugins in your project. Codex loads it when a task matches its description.

Can I use Performing Memory Forensics With Volatility3 Plugins in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-memory-forensics-with-volatility3-plugins -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-memory-forensics-with-volatility3-plugins, .gemini/skills/performing-memory-forensics-with-volatility3-plugins, .github/skills/performing-memory-forensics-with-volatility3-plugins and .opencode/skills/performing-memory-forensics-with-volatility3-plugins in your project.

What does Performing Memory Forensics With Volatility3 Plugins need to run?

Going by SKILL.md and its folder, Performing Memory Forensics With Volatility3 Plugins needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Performing Memory Forensics With Volatility3 Plugins access the network?

SKILL.md names 4 domains. As links in the text: volatilityfoundation.org, github.com, newtonpaul.com and attack.mitre.org. This is read from the text; nothing was executed.

Is Performing Memory Forensics With Volatility3 Plugins safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Memory Forensics With Volatility3 Plugins use?

Performing Memory Forensics With Volatility3 Plugins is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Memory Forensics With Volatility3 Plugins use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 926 tokens, read only when the agent opens those files.

What are the alternatives to Performing Memory Forensics With Volatility3 Plugins?

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Who maintains Performing Memory Forensics With Volatility3 Plugins?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,870 GitHub stars. The repository holds 639 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.