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extracting-memory-artifacts-with-rekall

extracting-memory-artifacts-with-rekall

github.com/mukul975Security35.2k53 installsUpdated 8w ago
Repository

About extracting-memory-artifacts-with-rekall

A Rekall memory forensics skill for security testers examining memory dumps for signs of compromise. It covers hidden processes, code injection, network connections, and extraction of suspicious DLLs and drivers.

Key features

  1. 01Load memory images and detect profiles
  2. 02Compare process lists to find hidden processes
  3. 03Detect injected or hollowed code
  4. 04Examine network connections
  5. 05Extract suspicious DLLs and drivers

Use cases

  1. 01Analyze malware samples in a controlled lab
  2. 02Investigate compromise indicators in memory dumps
  3. 03Build detections informed by offensive techniques

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill extracting-memory-artifacts-with-rekall

After installing, type/extracting-memory-artifacts-with-rekallin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

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

Details

From the project README

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}")

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