/analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor)
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-memory-forensics-with-lime-and-volatility --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/analyzing-memory-forensics-with-lime-and-volatility
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Performs Linux memory acquisition using LiME (Linux Memory Extractor)
SKILL.md
analyzing-memory-forensics-with-lime-and-volatility.SKILL.mdname: analyzing-memory-forensics-with-lime-and-volatility
description: 'Performs Linux memory acquisition using LiME (Linux Memory Extractor)
kernel module and analysis with Volatility 3 framework. Extracts process lists,
network connections, bash history, loaded kernel modules, and injected code from
Linux memory images. Use when performing incident response on compromised Linux
systems.
'
domain: cybersecurity
subdomain: security-operations
tags:
- memory-forensics
- linux-forensics
- lime
- volatility
- incident-response
- kernel-modules
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.CM-01
- RS.MA-01
- GV.OV-01
- DE.AE-02
mitre_attack:
- T1055
- T1003.001
- T1620
- T1564.001
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.sockstat
import 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
Read more
name: analyzing-memory-forensics-with-lime-and-volatility description: 'Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems. ' domain: cybersecurity subdomain: security-operations tags: - memory-forensics - linux-forensics - lime - volatility - incident-response - kernel-modules version: '1.0' author: mahipal license: Apache-2.0 nist_csf: - DE.CM-01 - RS.MA-01 - GV.OV-01 - DE.AE-02 mitre_attack: - T1055 - T1003.001 - T1620 - T1564.001
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.sockstat
import 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
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 standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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