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

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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.

oyi77 By oyi77 schedule Updated 6/8/2026

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

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

When NOT to Use

  • You need to perform the attack, not analyze it (use performing-* skills)
  • Task is about detection, not analysis (use detecting-* skills)
  • You need to implement controls (use implementing-* skills)
  • Task is about threat hunting, not post-incident analysis (use hunting-* skills)
  • You don't have access to the artifacts/logs to analyze
  • Task requires real-time monitoring (use SOC tools)

Red Flags

  • Performing actions without explicit written authorization from the asset owner
  • Testing against production systems without a defined scope and rules of engagement
  • Failing to use write-blockers when acquiring forensic evidence
  • Not verifying hash integrity before and after imaging
  • Modifying original evidence during analysis

Verification

  • All steps executed successfully against a test environment before production use
  • Output documented with screenshots or logs demonstrating expected behavior
  • Hash values computed and verified match between source and image
  • Chain of custody log complete with timestamps and examiner names
  • Analysis tools and versions documented for reproducibility
Install via CLI
npx skills add https://github.com/oyi77/1ai-skills --skill analyzing-memory-forensics-with-lime-and-volatility
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