extracting-memory-artifacts-with-rekall

name	extracting-memory-artifacts-with-rekall
description	Uses Rekall memory forensics framework to analyze memory dumps for process hollowing, injected code via VAD anomalies, hidden processes, and rootkit detection. Applies plugins like pslist, psscan, vadinfo, malfind, and dlllist to extract forensic artifacts from Windows memory images. Use during incident response memory analysis.
domain	cybersecurity
subdomain	security-operations
tags	["memory-forensics","rekall","process-hollowing","code-injection","vad-analysis","incident-response","security-operations"]
version	1.0
author	mahipal
license	Apache-2.0
nist_csf	["DE.CM-01","RS.MA-01","GV.OV-01","DE.AE-02"]
mitre_attack	["T1078","T1190","T1059","T1055","T1005"]
source	https://github.com/mukul975/Anthropic-Cybersecurity-Skills
source_commit	04450304b12645cb2b974ab96d28c0664758a88d

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.

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

from rekall import session
s = session.Session(filename="memory.raw")
# Compare pslist vs psscan for hidden processes
pslist_pids = set(p.pid for p  s.plugins.pslist())
psscan_pids = (p.pid  p  s.plugins.psscan())
hidden = psscan_pids - pslist_pids
()