Communitygithub.com

SamuelChien/mega-skills-collection

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

mega-skills-collection とは?

mega-skills-collection is a Claude Code agent skill that create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/SamuelChien/mega-skills-collection/tree/HEAD/acquiring-disk-image-with-dd-and-dcfldd

お気に入りのAIに質問する

このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

ドキュメント

Acquiring Disk Image with dd and dcfldd

When to Use

  • When you need to create a forensic copy of a suspect drive for investigation
  • During incident response when preserving volatile disk evidence before analysis
  • When law enforcement or legal proceedings require a verified bit-for-bit copy
  • Before performing any destructive analysis on a storage device
  • When acquiring images from physical drives, USB devices, or memory cards

Prerequisites

  • Linux-based forensic workstation (SIFT, Kali, or any Linux distro)
  • dd (pre-installed on all Linux systems) or dcfldd (enhanced forensic version)
  • Write-blocker hardware or software write-blocking configured
  • Destination drive with sufficient storage (larger than source)
  • Root/sudo privileges on the forensic workstation
  • SHA-256 or MD5 hashing utilities (sha256sum, md5sum)

Workflow

Step 1: Identify the Target Device and Enable Write Protection

# List all connected block devices to identify the target
lsblk -o NAME,SIZE,TYPE,MOUNTPOINT,MODEL

# Verify the device details
fdisk -l /dev/sdb

# Enable software write-blocking (if no hardware blocker)
blockdev --setro /dev/sdb

# Verify read-only status
blockdev --getro /dev/sdb
# Output: 1 (means read-only is enabled)

# Alternatively, use udev rules for persistent write-blocking
echo 'SUBSYSTEM=="block", ATTRS{serial}=="WD-WCAV5H861234", ATTR{ro}="1"' > /etc/udev/rules.d/99-writeblock.rules
udevadm control --reload-rules

Step 2: Prepare the Destination and Document the Source

# Create case directory structure
mkdir -p /cases/case-2024-001/{images,hashes,logs,notes}

# Document source drive information
hdparm -I /dev/sdb > /cases/case-2024-001/notes/source_drive_info.txt

# Record the serial number and model
smartctl -i /dev/sdb >> /cases/case-2024-001/notes/source_drive_info.txt

# Pre-hash the source device
sha256sum /dev/sdb | tee /cases/case-2024-001/hashes/source_hash_before.txt

Step 3: Acquire the Image Using dd

# Basic dd acquisition with progress and error handling
dd if=/dev/sdb of=/cases/case-2024-001/images/evidence.dd \
   bs=4096 \
   conv=noerror,sync \
   status=progress 2>&1 | tee /cases/case-2024-001/logs/dd_acquisition.log

# For compressed images to save space
dd if=/dev/sdb bs=4096 conv=noerror,sync status=progress | \
   gzip -c > /cases/case-2024-001/images/evidence.dd.gz

# Using dd with a specific count for partial acquisition
dd if=/dev/sdb of=/cases/case-2024-001/images/first_1gb.dd \
   bs=1M count=1024 status=progress

Step 4: Acquire Using dcfldd (Preferred Forensic Method)

# Install dcfldd if not present
apt-get install dcfldd

# Acquire image with built-in hashing and split output
dcfldd if=/dev/sdb \
   of=/cases/case-2024-001/images/evidence.dd \
   hash=sha256,md5 \
   hashwindow=1G \
   hashlog=/cases/case-2024-001/hashes/acquisition_hashes.txt \
   bs=4096 \
   conv=noerror,sync \
   errlog=/cases/case-2024-001/logs/dcfldd_errors.log

# Split large images into manageable segments
dcfldd if=/dev/sdb \
   of=/cases/case-2024-001/images/evidence.dd \
   hash=sha256 \
   hashlog=/cases/case-2024-001/hashes/split_hashes.txt \
   bs=4096 \
   split=2G \
   splitformat=aa

# Acquire with verification pass
dcfldd if=/dev/sdb \
   of=/cases/case-2024-001/images/evidence.dd \
   hash=sha256 \
   hashlog=/cases/case-2024-001/hashes/verification.txt \
   vf=/cases/case-2024-001/images/evidence.dd \
   verifylog=/cases/case-2024-001/logs/verify.log

Step 5: Verify Image Integrity

# Hash the acquired image
sha256sum /cases/case-2024-001/images/evidence.dd | \
   tee /cases/case-2024-001/hashes/image_hash.txt

# Compare source and image hashes
diff <(sha256sum /dev/sdb | awk '{print $1}') \
     <(sha256sum /cases/case-2024-001/images/evidence.dd | awk '{print $1}')

# If using split images, verify each segment
sha256sum /cases/case-2024-001/images/evidence.dd.* | \
   tee /cases/case-2024-001/hashes/split_image_hashes.txt

# Re-hash source to confirm no changes occurred
sha256sum /dev/sdb | tee /cases/case-2024-001/hashes/source_hash_after.txt
diff /cases/case-2024-001/hashes/source_hash_before.txt \
     /cases/case-2024-001/hashes/source_hash_after.txt

Step 6: Document the Acquisition Process

# Generate acquisition report
cat << 'EOF' > /cases/case-2024-001/notes/acquisition_report.txt
DISK IMAGE ACQUISITION REPORT
==============================
Case Number: 2024-001
Date/Time: $(date -u +"%Y-%m-%d %H:%M:%S UTC")
Examiner: [Name]

Source Device: /dev/sdb
Model: [from hdparm output]
Serial: [from hdparm output]
Size: [from fdisk output]

Acquisition Tool: dcfldd v1.9.1
Block Size: 4096
Write Blocker: [Hardware/Software model]

Image File: evidence.dd
Image Hash (SHA-256): [from hash file]
Source Hash (SHA-256): [from hash file]
Hash Match: YES/NO

Errors During Acquisition: [from error log]
EOF

# Compress logs for archival
tar -czf /cases/case-2024-001/acquisition_package.tar.gz \
   /cases/case-2024-001/hashes/ \
   /cases/case-2024-001/logs/ \
   /cases/case-2024-001/notes/

Key Concepts

ConceptDescription
Bit-for-bit copyExact replica of source including unallocated space and slack space
Write blockerHardware or software mechanism preventing writes to evidence media
Hash verificationCryptographic hash comparing source and image to prove integrity
Block size (bs)Transfer chunk size affecting speed; 4096 or 64K typical for forensics
conv=noerror,syncContinue on read errors and pad with zeros to maintain offset alignment
Chain of custodyDocumented trail proving evidence has not been tampered with
Split imagingBreaking large images into smaller files for storage and transport
Raw/dd formatBit-for-bit image format without metadata container overhead

Tools & Systems

ToolPurpose
ddStandard Unix disk duplication utility for raw imaging
dcflddDoD Computer Forensics Laboratory enhanced version of dd with hashing
dc3ddAnother forensic dd variant from the DoD Cyber Crime Center
sha256sumSHA-256 hash calculation for integrity verification
blockdevLinux command to set block device read-only mode
hdparmDrive identification and parameter reporting
smartctlS.M.A.R.T. data retrieval for drive health and identification
lsblkBlock device enumeration and identification

Common Scenarios

Scenario 1: Acquiring a Suspect Laptop Hard Drive Connect the drive via a Tableau T35u hardware write-blocker, identify as /dev/sdb, use dcfldd with SHA-256 hashing, split into 4GB segments for DVD archival, verify hashes match, document in case notes.

Scenario 2: Imaging a USB Flash Drive from a Compromised Workstation Use software write-blocking with blockdev --setro, acquire with dcfldd including MD5 and SHA-256 dual hashing, image is small enough for single file, verify and store on encrypted case drive.

Scenario 3: Remote Acquisition Over Network Use dd piped through netcat or ssh for remote acquisition: ssh root@remote "dd if=/dev/sda bs=4096" | dd of=remote_image.dd bs=4096, hash both ends independently to verify transfer integrity.

Scenario 4: Acquiring from a Failing Drive Use ddrescue first to recover readable sectors, then use dd with conv=noerror,sync to fill gaps with zeros, document which sectors were unreadable in the error log.

Output Format

Acquisition Summary:
  Source:       /dev/sdb (500GB Western Digital WD5000AAKX)
  Destination:  /cases/case-2024-001/images/evidence.dd
  Tool:         dcfldd 1.9.1
  Block Size:   4096 bytes
  Duration:     2h 15m 32s
  Bytes Copied: 500,107,862,016
  Errors:       0 bad sectors
  Source SHA-256:  a3f2b8c9d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1
  Image SHA-256:   a3f2b8c9d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1
  Verification:    PASSED - Hashes match

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

SamuelChien/mega-skills-collection

Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.

SamuelChien/mega-skills-collection

Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.

SamuelChien/mega-skills-collection

Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.

SamuelChien/mega-skills-collection

Estratega de Inteligencia de Dominio de Andru.ia. Analiza el nicho específico de un proyecto para inyectar conocimientos, regulaciones y estándares únicos del sector. Actívalo tras definir el nicho.

SamuelChien/mega-skills-collection

2D game development principles. Sprites, tilemaps, physics, camera.

SamuelChien/mega-skills-collection

3D game development principles. Rendering, shaders, physics, cameras.

SamuelChien/mega-skills-collection

Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences.

SamuelChien/mega-skills-collection

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

SamuelChien/mega-skills-collection

Ab Test Analyzer - Auto-activating skill for Data Analytics. Triggers on: ab test analyzer, ab test analyzer Part of the Data Analytics skill category.

SamuelChien/mega-skills-collection

A B Test Config Creator - Auto-activating skill for ML Deployment. Triggers on: a b test config creator, a b test config creator Part of the ML Deployment skill category.

SamuelChien/mega-skills-collection

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

SamuelChien/mega-skills-collection

Acceptance Criteria Creator - Auto-activating skill for Enterprise Workflows. Triggers on: acceptance criteria creator, acceptance criteria creator Part of the Enterprise Workflows skill category.

SamuelChien/mega-skills-collection

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

SamuelChien/mega-skills-collection

Accessibility Audit Runner - Auto-activating skill for Frontend Development. Triggers on: accessibility audit runner, accessibility audit runner Part of the Frontend Development skill category.

SamuelChien/mega-skills-collection

You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.

SamuelChien/mega-skills-collection

Web accessibility patterns for WCAG 2.2 compliance including ARIA, keyboard navigation, screen readers, and testing

SamuelChien/mega-skills-collection

Manage Discord channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Discord channel.

SamuelChien/mega-skills-collection

Create unified specification packages across Business, Development, and Design teams. Staged elaboration (L0 Vision → L1 Requirements → L2 Team Detail → L3 Acceptance Criteria) to build shared understanding. Does not write code.

SamuelChien/mega-skills-collection

Research a company or person and get actionable sales intel. Works standalone with web search, supercharged when you connect enrichment tools or your CRM. Trigger with "research [company]", "look up [person]", "intel on [prospect]", "who is [name] at [company]", or "tell me about [company]".

SamuelChien/mega-skills-collection

Validate messaging consistency across website, GitHub repos, and local documentation generating read-only discrepancy reports. Use when checking content alignment or finding mixed messaging. Trigger with phrases like "check consistency", "validate documentation", or "audit messaging".

関連スキル