# extracting-memory-artifacts-with-rekall

> Analyze Windows memory dumps for signs of compromise using the Rekall memory forensics framework, including process injection, hidden processes, and rootkit detection.

- Skill: `mukul975/extracting-memory-artifacts-with-rekall` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add mukul975/extracting-memory-artifacts-with-rekall`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mukul975/extracting-memory-artifacts-with-rekall/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security, Coding & Dev Tools, Data & Analytics, Incident Response, Penetration Testing
- Tags: Forensic Artifacts, Incident Response, Malware Analysis, Memory Analysis, Memory Forensics, Rekall, Windows
- License: Apache-2.0
- Author: mukul975 (https://skillmd.com/u/mukul975)
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/mukul975/extracting-memory-artifacts-with-rekall

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# Extracting Memory Artifacts with Rekall


## When to Use

- When performing authorized security testing that involves extracting memory artifacts with rekall
- When analyzing malware samples or attack artifacts in a controlled environment
- When conducting red team exercises or penetration testing engagements
- When building detection capabilities based on offensive technique understanding

## 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

Use Rekall to analyze memory dumps for signs of compromise including process
injection, hidden processes, and suspicious network connections.

```python
from rekall import session
from rekall import plugins

# Create a Rekall session with a memory image
s = session.Session(
    filename="/path/to/memory.raw",
    autodetect=["rsds"],
    profile_path=["https://github.com/google/rekall-profiles/raw/master"]
)

# List processes
for proc in s.plugins.pslist():
    print(proc)

# Detect injected code
for result in s.plugins.malfind():
    print(result)
```

Key analysis steps:
1. Load memory image and auto-detect profile
2. Run pslist and psscan to find hidden processes
3. Use malfind to detect injected/hollowed code in process VADs
4. Examine network connections with netscan
5. Extract suspicious DLLs and drivers with dlllist/modules

## Examples

```python
from rekall import session
s = session.Session(filename="memory.raw")
# Compare pslist vs psscan for hidden processes
pslist_pids = set(p.pid for p in s.plugins.pslist())
psscan_pids = set(p.pid for p in s.plugins.psscan())
hidden = psscan_pids - pslist_pids
print(f"Hidden PIDs: {hidden}")
```