
Analyzing Memory Forensics With Lime And Volatility
- 261 installs
- 27.3k repo stars
- Updated August 2, 2026
- mukul975/anthropic-cybersecurity-skills
Analyzing Memory Forensics with LiME and Volatility is an agent skill that walks through Linux memory acquisition and Volatility-based analysis for security investigations.
About
Analyzing Memory Forensics with LiME and Volatility is an agent skill aimed at builders and small teams who need to investigate suspected compromise on Linux systems using industry-standard memory capture and framework-based analysis. The catalog entry ships under Apache License 2.0 as part of a cybersecurity skills bundle; invoke it when you must preserve volatile evidence, parse process and network artifacts from a RAM image, and document findings for security review. It is advanced, hands-on work—expect kernel modules, analyst tooling, and careful chain-of-custody—not a substitute for a full SOC. Prism lists it so solo operators shipping APIs or internal services have a procedural anchor when escalating from app logs to host memory forensics during Ship-phase security reviews or post-incident validation.
- Memory forensics workflow oriented around LiME acquisition and Volatility analysis
- Supports incident response when disk logs are insufficient or malware is memory-resident
- Fits Anthropic cybersecurity skills collection patterns for structured investigation steps
- Apache 2.0 licensed skill package suitable for audit and compliance contexts
Analyzing Memory Forensics With Lime And Volatility by the numbers
- 261 all-time installs (skills.sh)
- +15 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #661 of 2,203 Security skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 261 |
|---|---|
| repo stars | ★ 27.3k |
| Security audit | 1 / 3 scanners passed |
| Last updated | August 2, 2026 |
| Repository | mukul975/anthropic-cybersecurity-skills ↗ |
What it does
Guide memory acquisition with LiME and analysis with Volatility when investigating compromised hosts or validating incident hypotheses.
Who is it for?
Best when you're performing structured Linux memory forensics during a security incident or pre-launch hardening review.
Skip if: Routine application debugging, Cloudflare Worker-only stacks with no OS memory to image, or teams without legal authority to capture host memory.
When should I use this skill?
Investigating Linux host compromise or validating memory-resident threats when LiME capture and Volatility analysis are appropriate.
What you get
You obtain a memory image with LiME-compatible capture and analyze it with Volatility-oriented steps to support incident conclusions and remediation.
- Memory capture procedure notes
- Volatility-oriented analysis findings for remediation
Files
Analyzing Memory Forensics with LiME and Volatility
When to Use
- When investigating security incidents that require analyzing memory forensics with lime and volatility
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
Acquire Linux memory using LiME kernel module, then analyze with Volatility 3 to extract forensic artifacts from the memory image.
# LiME acquisition
insmod lime-$(uname -r).ko "path=/evidence/memory.lime format=lime"
# Volatility 3 analysis
vol3 -f /evidence/memory.lime linux.pslist
vol3 -f /evidence/memory.lime linux.bash
vol3 -f /evidence/memory.lime linux.sockstatimport volatility3
from volatility3.framework import contexts, automagic
from volatility3.plugins.linux import pslist, bash, sockstat
# Programmatic Volatility 3 usage
context = contexts.Context()
automagics = automagic.available(context)Key analysis steps: 1. Acquire memory with LiME (format=lime or format=raw) 2. List processes with linux.pslist, compare with linux.psscan 3. Extract bash command history with linux.bash 4. List network connections with linux.sockstat 5. Check loaded kernel modules with linux.lsmod for rootkits
Examples
# Full forensic workflow
vol3 -f memory.lime linux.pslist | grep -v "\[kthread\]"
vol3 -f memory.lime linux.bash
vol3 -f memory.lime linux.malfind
vol3 -f memory.lime linux.lsmod
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API Reference: Analyzing Memory Forensics with LiME and Volatility
LiME (Linux Memory Extractor)
# Build LiME module
cd LiME/src && make
# Acquire memory (lime format - includes metadata)
insmod lime-$(uname -r).ko "path=/evidence/mem.lime format=lime"
# Acquire memory (raw format)
insmod lime-$(uname -r).ko "path=/evidence/mem.raw format=raw"
# Acquire over network
insmod lime.ko "path=tcp:4444 format=lime"
# On forensic workstation: nc target 4444 > mem.limeVolatility 3 Linux Plugins
| Plugin | Description |
|---|---|
linux.pslist | List processes via task_struct |
linux.psscan | Brute-force scan for task_struct |
linux.bash | Recovered bash command history |
linux.sockstat | Network connections |
linux.lsmod | Loaded kernel modules |
linux.malfind | Detect injected code |
linux.check_afinfo | Detect network hooking |
linux.tty_check | Detect TTY hooking |
linux.proc.Maps | Process memory maps |
Volatility 3 CLI
vol3 -f memory.lime linux.pslist
vol3 -f memory.lime linux.bash
vol3 -f memory.lime linux.sockstat
vol3 -f memory.lime linux.malfind
vol3 -f memory.lime linux.lsmod
vol3 -f memory.lime linux.check_afinfoHidden Process Detection
# Compare pslist (linked list) vs psscan (brute force)
vol3 -f mem.lime linux.pslist > pslist.txt
vol3 -f mem.lime linux.psscan > psscan.txt
diff pslist.txt psscan.txtReferences
- LiME: https://github.com/504ensicsLabs/LiME
- Volatility 3: https://github.com/volatilityfoundation/volatility3
- Volatility 3 docs: https://volatility3.readthedocs.io/
#!/usr/bin/env python3
"""Agent for Linux memory forensics using LiME acquisition and Volatility 3."""
import json
import subprocess
import argparse
from datetime import datetime
from pathlib import Path
def acquire_memory_lime(output_path, lime_format="lime"):
"""Acquire memory using LiME kernel module."""
kernel_version = subprocess.run(
["uname", "-r"], capture_output=True, text=True, timeout=120
).stdout.strip()
lime_module = f"lime-{kernel_version}.ko"
if not Path(lime_module).exists():
lime_module = "lime.ko"
cmd = ["insmod", lime_module, f"path={output_path}", f"format={lime_format}"]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
return {
"status": "success" if result.returncode == 0 else "failed",
"output_path": output_path,
"format": lime_format,
"kernel": kernel_version,
"stderr": result.stderr,
}
def run_vol3_plugin(image_path, plugin_name, extra_args=None):
"""Run a Volatility 3 plugin and capture output."""
cmd = ["vol3", "-f", image_path, plugin_name]
if extra_args:
cmd.extend(extra_args)
try:
result = subprocess.run(
cmd, capture_output=True, text=True, timeout=300,
)
lines = result.stdout.strip().splitlines()
return {"plugin": plugin_name, "output": lines, "error": result.stderr.strip()}
except subprocess.TimeoutExpired:
return {"plugin": plugin_name, "output": [], "error": "Timeout"}
def parse_pslist_output(lines):
"""Parse Volatility linux.pslist output into structured data."""
processes = []
for line in lines:
parts = line.split()
if len(parts) >= 4 and parts[0].isdigit():
processes.append({
"pid": int(parts[0]),
"ppid": int(parts[1]) if parts[1].isdigit() else 0,
"name": parts[-1],
})
return processes
def list_processes(image_path):
"""List all processes from memory image."""
result = run_vol3_plugin(image_path, "linux.pslist")
return parse_pslist_output(result.get("output", []))
def extract_bash_history(image_path):
"""Extract bash command history from memory."""
result = run_vol3_plugin(image_path, "linux.bash")
commands = []
for line in result.get("output", []):
parts = line.split(None, 3)
if len(parts) >= 4 and parts[0].isdigit():
commands.append({
"pid": int(parts[0]),
"name": parts[1],
"timestamp": parts[2] if len(parts) > 2 else "",
"command": parts[3] if len(parts) > 3 else "",
})
return commands
def list_network_connections(image_path):
"""List network connections from memory."""
result = run_vol3_plugin(image_path, "linux.sockstat")
connections = []
for line in result.get("output", []):
if "TCP" in line or "UDP" in line:
connections.append(line.strip())
return connections
def list_kernel_modules(image_path):
"""List loaded kernel modules to detect rootkits."""
result = run_vol3_plugin(image_path, "linux.lsmod")
modules = []
for line in result.get("output", []):
parts = line.split()
if parts and not parts[0].startswith("Offset"):
modules.append({"name": parts[-1] if parts else line.strip()})
return modules
def detect_hidden_processes(image_path):
"""Compare pslist vs psscan to find hidden processes."""
pslist = run_vol3_plugin(image_path, "linux.pslist")
psscan = run_vol3_plugin(image_path, "linux.psscan")
pslist_pids = set()
for line in pslist.get("output", []):
parts = line.split()
if parts and parts[0].isdigit():
pslist_pids.add(int(parts[0]))
hidden = []
for line in psscan.get("output", []):
parts = line.split()
if parts and parts[0].isdigit():
pid = int(parts[0])
if pid not in pslist_pids and pid > 0:
hidden.append({"pid": pid, "line": line.strip()})
return hidden
def detect_suspicious_commands(bash_history):
"""Flag suspicious commands in bash history."""
suspicious_patterns = [
"curl.*|.*sh", "wget.*&&.*chmod", "base64.*-d",
"nc.*-e", "python.*-c.*import.*socket",
"nohup", "rm.*-rf.*/var/log", "history.*-c",
"iptables.*-F", "chmod.*777", "chattr.*-i",
]
import re
findings = []
for entry in bash_history:
cmd = entry.get("command", "")
for pattern in suspicious_patterns:
if re.search(pattern, cmd, re.IGNORECASE):
findings.append({
"pid": entry["pid"],
"command": cmd,
"pattern": pattern,
"severity": "HIGH",
})
break
return findings
def check_malfind(image_path):
"""Run malfind to detect injected code."""
result = run_vol3_plugin(image_path, "linux.malfind")
return result.get("output", [])
def main():
parser = argparse.ArgumentParser(description="LiME + Volatility 3 Forensics Agent")
parser.add_argument("--image", help="Path to memory image")
parser.add_argument("--acquire", help="Output path for LiME acquisition")
parser.add_argument("--output", default="memory_forensics_report.json")
parser.add_argument("--action", choices=[
"acquire", "pslist", "bash", "network", "modules",
"hidden", "malfind", "full_analysis"
], default="full_analysis")
args = parser.parse_args()
report = {"generated_at": datetime.utcnow().isoformat(), "findings": {}}
if args.action == "acquire" and args.acquire:
result = acquire_memory_lime(args.acquire)
report["findings"]["acquisition"] = result
print(f"[+] Memory acquisition: {result['status']}")
return
if not args.image:
print("[-] --image required for analysis actions")
return
if args.action in ("pslist", "full_analysis"):
procs = list_processes(args.image)
report["findings"]["processes"] = procs
print(f"[+] Processes: {len(procs)}")
if args.action in ("bash", "full_analysis"):
history = extract_bash_history(args.image)
report["findings"]["bash_history"] = history
suspicious = detect_suspicious_commands(history)
report["findings"]["suspicious_commands"] = suspicious
print(f"[+] Bash commands: {len(history)}, Suspicious: {len(suspicious)}")
if args.action in ("network", "full_analysis"):
conns = list_network_connections(args.image)
report["findings"]["connections"] = conns
print(f"[+] Network connections: {len(conns)}")
if args.action in ("modules", "full_analysis"):
modules = list_kernel_modules(args.image)
report["findings"]["kernel_modules"] = modules
print(f"[+] Kernel modules: {len(modules)}")
if args.action in ("hidden", "full_analysis"):
hidden = detect_hidden_processes(args.image)
report["findings"]["hidden_processes"] = hidden
print(f"[+] Hidden processes: {len(hidden)}")
with open(args.output, "w") as f:
json.dump(report, f, indent=2, default=str)
print(f"[+] Report saved to {args.output}")
if __name__ == "__main__":
main()
Related skills
How it compares
Forensic investigation workflow for RAM images, not a passive dependency scanner or generic code review skill.
FAQ
Who is analyzing-memory-forensics-with-lime-and-volatility for?
Developers and operators who must perform or oversee Linux memory forensics using LiME and Volatility during security incidents or audits.
When should I use analyzing-memory-forensics-with-lime-and-volatility?
During Ship security work or Operate incident response when you need volatile evidence from a Linux system and structured Volatility analysis.
Is analyzing-memory-forensics-with-lime-and-volatility safe to install?
Treat it as high-privilege forensic tooling—review the Security Audits panel on this page and only run capture/analysis on systems you own or are authorized to examine.