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

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 ima...

Cybersecurity
MITRE ATT&CK
OWASP
By Anthropic Cybersecurity Skills (mukul975)
Target File: .cursor/rules/analyzing-memory-forensics-with-lime-and-volatility.mdc
Type @rule-name in Cursor Chat
---
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 ima...
globs: *
alwaysApply: true
---

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

# 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.

```bash
# 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
```

```python
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

```bash
# 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
```

How to use this skill

  • • Cursor (Modern): Download as analyzing-memory-forensics-with-lime-and-volatility.mdc and place it inside your project's .cursor/rules/ folder.
  • • Claude Code: Append the instructions to your project's root CLAUDE.md file.
  • • Cline / Roo Code: Save or append to .clinerules in your project root.
  • • Windsurf: Save or append to .windsurfrules.
  • • CLI Quick Add: npx awesome-ai-tools add analyzing-memory-forensics-with-lime-and-volatility