What it does. 'Uses Rekall memory forensics framework to analyze memory dumps for process Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-memory-artifacts-with-rekall, or copy the skill folder into ~/.claude/skills/extracting-memory-artifacts-with-rekall/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/extracting-memory-artifacts-with-rekall/SKILL.md

SKILL.md (verbatim)

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

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 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}")

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Extracting Memory Artifacts with Rekall

Rekall Session

from rekall import session

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

Key Plugins

Hidden Process Detection

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

Malfind Output Fields

• pid: Process ID
• name: Process name
• address: VAD start address
• protection: Memory protection (PAGE_EXECUTE_READWRITE = suspicious)
• tag: Pool tag

Command Line

rekall -f memory.raw pslist
rekall -f memory.raw malfind
rekall -f memory.raw netscan
rekall -f memory.raw dlllist --pid 1234

References

• Rekall: https://github.com/google/rekall
• Rekall docs: https://rekall.readthedocs.io/
• Rekall profiles: https://github.com/google/rekall-profiles

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