Analyze memory dumps using Volatility3 plugins to detect injected code, rootkits, credential theft, and malware artifacts in Windows, Linux, and macOS memory images.
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node --versionperforming-memory-forensics-with-volatility3-pluginsExecute the skills CLI command in your project's root directory to begin installation:
Fetches performing-memory-forensics-with-volatility3-plugins from mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
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Restart Cursor to activate performing-memory-forensics-with-volatility3-plugins. Access via /performing-memory-forensics-with-volatility3-plugins in your agent's command palette.
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| 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 |
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))
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
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Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
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mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
mukul975/Anthropic-Cybersecurity-Skills
performing-memory-forensics-with-volatility3-plugins has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: performing-memory-forensics-with-volatility3-plugins is the kind of skill you can hand to a new teammate without a long onboarding doc.
performing-memory-forensics-with-volatility3-plugins is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in performing-memory-forensics-with-volatility3-plugins — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
performing-memory-forensics-with-volatility3-plugins is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in performing-memory-forensics-with-volatility3-plugins — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: performing-memory-forensics-with-volatility3-plugins is focused, and the summary matches what you get after install.
performing-memory-forensics-with-volatility3-plugins reduced setup friction for our internal harness; good balance of opinion and flexibility.
performing-memory-forensics-with-volatility3-plugins fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added performing-memory-forensics-with-volatility3-plugins from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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