external/anthropic-cybersecurity-skills/skills/performing-memory-forensics-with-volatility3-plugins/SKILL.md
Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.
npx skillsauth add seikaikyo/dash-skills performing-memory-forensics-with-volatility3-pluginsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
volatility3 framework installed.raw, .dmp, .vmem, .lime)#!/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))
development
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tools
需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。
development
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testing
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