analyzing-memory-forensics-with-lime-and-volatility
This Claude Code skill provides a structured procedure for acquiring Linux system memory using the LiME kernel module and analyzing the resulting memory image with Volatility 3 to extract forensic artifacts. Use this skill when investigating security incidents requiring memory forensics, building threat hunting procedures, training SOC analysts on memory analysis techniques, or validating detection coverage for memory-based attack indicators like hidden processes, rootkits, and command history.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills /tmp/analyzing-memory-forensics-with-lime-and-volatility && cp -r /tmp/analyzing-memory-forensics-with-lime-and-volatility/skills/analyzing-memory-forensics-with-lime-and-volatility ~/.claude/skills/analyzing-memory-forensics-with-lime-and-volatilitySKILL.md
# 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 ```
Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
Detect dangerous ACL misconfigurations in Active Directory using ldap3
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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
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Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query