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analyzing-memory-forensics-with-lime-and-volatility

Acquire Linux memory with LiME and examine it using Volatility 3. Extract processes, network connections, command history, loaded kernel modules, and signs of injected code to support investigations of compromised Linux systems.

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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.

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适用场景

  • Acquiring memory from a compromised Linux system
  • Comparing process lists to investigate hidden processes
  • Reviewing command history and network connections
  • Inspecting kernel modules and injected code

包含的文件(4)

  • SKILL.md2.5 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.6 KB
  • scripts/agent.py7.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

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