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Prompts Linux Memory Forensics with LiME and Volatility

security analyst security skill risk: medium

Linux Memory Forensics with LiME and Volatility

Acquire Linux memory using the LiME kernel module then analyze the image with Volatility 3 to extract processes, bash history, network connections, and kernel modules.

  • External action: medium

SKILL 4 files · 2 folders

SKILL.md
---
name: analyzing-memory-forensics-with-lime-and-volatility
description: "Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with 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
```

REQUIRED CONTEXT

  • Linux system with kernel module support
  • Volatility 3 installation

OPTIONAL CONTEXT

  • test/lab environment
  • Python 3.8+

EXPECTED OUTPUT

Format
markdown
Constraints
  • include bash and python code examples
  • list key analysis steps

EXAMPLES

Includes bash commands for LiME acquisition and Volatility analysis, a Python snippet, and a full forensic workflow example.

CAVEATS

Dependencies
  • 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
Missing context
  • Output format or report template
  • Error handling or failure modes
  • Tool version pinning for LiME and Volatility
Ambiguities
  • Python code snippet is incomplete and does not demonstrate full usage or integration.
  • Does not specify desired output format or structure for analysis results.

QUALITY

OVERALL
0.65
CLARITY
0.82
SPECIFICITY
0.68
REUSABILITY
0.55
COMPLETENESS
0.58

IMPROVEMENT SUGGESTIONS

  • Add an explicit 'Output Format' section describing how results should be returned.
  • Introduce template placeholders (e.g., {{evidence_path}}) for reusable parameters.
  • Include a short validation or success-criteria checklist at the end.

USAGE

Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.

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