Asi performing-memory-forensics-with-volatility3-plugins
Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.
install
source · Clone the upstream repo
git clone https://github.com/plurigrid/asi
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/plurigrid/asi "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/asi/skills/performing-memory-forensics-with-volatility3-plugins" ~/.claude/skills/plurigrid-asi-performing-memory-forensics-with-volatility3-plugins && rm -rf "$T"
manifest:
plugins/asi/skills/performing-memory-forensics-with-volatility3-plugins/SKILL.mdtags
source content
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
framework installedvolatility3 - 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