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Acquiring Disk Image With Dd And Dcfldd

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mukul975
acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

Overview

Publishermukul975
RepositoryAnthropic-Cybersecurity-Skills
Skill nameacquiring-disk-image-with-dd-and-dcfldd
Stars
32.9K
Forks
4K
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mukul975 on GitHub. Read the source before you install it.

Installation

Install the Acquiring Disk Image With Dd And Dcfldd AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills
mkdir -p .claude/skills
cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/acquiring-disk-image-with-dd-and-dcfldd .claude/skills/acquiring-disk-image-with-dd-and-dcfldd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acquiring Disk Image With Dd And Dcfldd in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acquiring Disk Image With Dd And Dcfldd on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acquiring Disk Image With Dd And Dcfldd is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
# 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

bash
# 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

bash
# 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)

bash
# 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

bash
# 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

bash
# 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Acquiring Disk Image With Dd And Dcfldd AI skill do?

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

Why use Acquiring Disk Image With Dd And Dcfldd on TypingMind?

Because you install it once and use it with any model. Acquiring Disk Image With Dd And Dcfldd is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acquiring Disk Image With Dd And Dcfldd in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/acquiring-disk-image-with-dd-and-dcfldd. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Acquiring Disk Image With Dd And Dcfldd?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acquiring Disk Image With Dd And Dcfldd?

As many as you like. As long as a model supports skills, you can use Acquiring Disk Image With Dd And Dcfldd with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acquiring Disk Image With Dd And Dcfldd AI skill free?

Yes. It is published on GitHub by mukul975 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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