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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