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