analyzing-disk-image-with-autopsy

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-disk-image-with-autopsy
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summary

Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.

skill.md
name
analyzing-disk-image-with-autopsy
description
Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
domain
cybersecurity
subdomain
digital-forensics
tags
- forensics - autopsy - disk-analysis - sleuth-kit - file-recovery - artifact-analysis
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- RS.AN-01 - RS.AN-03 - DE.AE-02 - RS.MA-01

Analyzing Disk Image with Autopsy

When to Use

  • When you have a forensic disk image and need structured analysis of its contents
  • During investigations requiring file recovery, keyword searching, and timeline analysis
  • When non-technical stakeholders need visual reports from forensic evidence
  • For examining file system metadata, deleted files, and embedded artifacts
  • When building a comprehensive case from multiple disk images

Prerequisites

  • Autopsy 4.x installed (Windows) or Autopsy 4.x with The Sleuth Kit (Linux)
  • Forensic disk image in raw (dd), E01 (EnCase), or AFF format
  • Minimum 8GB RAM (16GB recommended for large images)
  • Java Runtime Environment (JRE) 8+ for Autopsy
  • Sufficient disk space for the Autopsy case database (2-3x image size)
  • Hash databases (NSRL, known-bad hashes) for file identification

Workflow

Step 1: Install Autopsy and Configure Environment

# On Linux, install Sleuth Kit and Autopsy
sudo apt-get install autopsy sleuthkit

# Download Autopsy 4.x (GUI version) from official source
wget https://github.com/sleuthkit/autopsy/releases/download/autopsy-4.21.0/autopsy-4.21.0.zip
unzip autopsy-4.21.0.zip -d /opt/autopsy

# On Windows, run the MSI installer from sleuthkit.org
# Launch Autopsy
/opt/autopsy/bin/autopsy --nosplash

# For Sleuth Kit command-line analysis alongside Autopsy
sudo apt-get install sleuthkit

Step 2: Create a New Case and Add the Disk Image

1. Launch Autopsy > "New Case"
2. Enter Case Name: "CASE-2024-001-Workstation"
3. Set Base Directory: /cases/case-2024-001/autopsy/
4. Enter Case Number, Examiner Name
5. Click "Add Data Source"
6. Select "Disk Image or VM File"
7. Browse to: /cases/case-2024-001/images/evidence.dd
8. Select Time Zone of the original system
9. Configure Ingest Modules (see Step 3)
# Alternatively, use Sleuth Kit CLI to verify the image first
img_stat /cases/case-2024-001/images/evidence.dd

# List partitions in the image
mmls /cases/case-2024-001/images/evidence.dd

# Output example:
# DOS Partition Table
# Offset Sector: 0
# Units are in 512-byte sectors
#      Slot    Start        End          Length       Description
#      00:  -----   0000000000   0000002047   0000002048   Primary Table (#0)
#      01:  00:00   0000002048   0001026047   0001024000   NTFS (0x07)
#      02:  00:01   0001026048   0976771071   0975745024   NTFS (0x07)

# List files in a partition (offset 2048 sectors)
fls -o 2048 /cases/case-2024-001/images/evidence.dd

Step 3: Configure and Run Ingest Modules

Enable the following Autopsy Ingest Modules:
- Recent Activity: Extracts browser history, downloads, cookies, bookmarks
- Hash Lookup: Compares files against NSRL and known-bad hash sets
- File Type Identification: Identifies files by signature, not extension
- Keyword Search: Indexes content for full-text searching
- Email Parser: Extracts emails from PST, MBOX, EML files
- Extension Mismatch Detector: Finds files with wrong extensions
- Exif Parser: Extracts metadata from images (GPS, camera, timestamps)
- Encryption Detection: Identifies encrypted files and containers
- Interesting Files Identifier: Flags files matching custom rule sets
- Embedded File Extractor: Extracts files from ZIP, Office docs, PDFs
- Picture Analyzer: Categorizes images using PhotoDNA or hash matching
- Data Source Integrity: Verifies image hash during ingest
# Configure NSRL hash set for known-good filtering
# Download NSRL from https://www.nist.gov/itl/ssd/software-quality-group/national-software-reference-library-nsrl
wget https://s3.amazonaws.com/rds.nsrl.nist.gov/RDS/current/rds_modernm.zip
unzip rds_modernm.zip -d /opt/autopsy/hashsets/

# Import into Autopsy:
# Tools > Options > Hash Sets > Import > Select NSRLFile.txt
# Mark as "Known" (to filter out known-good files)

Step 4: Analyze File System and Recover Deleted Files

# In Autopsy GUI: Navigate tree structure
# - Data Sources > evidence.dd > vol2 (NTFS)
# - Examine directory tree, note deleted files (marked with X)

# Using Sleuth Kit CLI for targeted recovery
# List deleted files
fls -rd -o 2048 /cases/case-2024-001/images/evidence.dd

# Recover a specific deleted file by inode
icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523 > /cases/case-2024-001/recovered/deleted_document.docx

# Extract all files from a directory
tsk_recover -o 2048 -d /Users/suspect/Documents \
   /cases/case-2024-001/images/evidence.dd \
   /cases/case-2024-001/recovered/documents/

# Get detailed file metadata
istat -o 2048 /cases/case-2024-001/images/evidence.dd 14523
# Shows: creation, modification, access, MFT change timestamps, size, data runs

Step 5: Perform Keyword Searches and Tag Evidence

In Autopsy:
1. Keyword Search panel > "Ad Hoc Keyword Search"
2. Search terms: credit card patterns, SSN regex, email addresses
3. Example regex for credit cards: \b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14})\b
4. Example regex for SSN: \b\d{3}-\d{2}-\d{4}\b
5. Review results > Right-click items > "Add Tag"
6. Create tags: "Evidence-Critical", "Evidence-Supporting", "Requires-Review"
7. Add comments to tagged items documenting relevance
# Using Sleuth Kit for CLI keyword search
srch_strings -a -o 2048 /cases/case-2024-001/images/evidence.dd | \
   grep -iE '(password|secret|confidential)' > /cases/case-2024-001/keyword_hits.txt

# Search for specific file signatures
sigfind -o 2048 /cases/case-2024-001/images/evidence.dd 25504446
# 25504446 = %PDF header signature

Step 6: Build Timeline and Generate Reports

In Autopsy:
1. Timeline viewer: Tools > Timeline
2. Select date range of interest (incident window)
3. Filter by event type: File Created, Modified, Accessed, Web Activity
4. Zoom into suspicious time periods
5. Export timeline events as CSV for external analysis

Generate Report:
1. Generate Report > HTML Report
2. Select tagged items and data sources to include
3. Configure report sections: file listings, keyword hits, timeline
4. Export to /cases/case-2024-001/reports/
# Using Sleuth Kit mactime for CLI timeline
fls -r -m "/" -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/bodyfile.txt

# Generate timeline from bodyfile
mactime -b /cases/case-2024-001/bodyfile.txt -d > /cases/case-2024-001/timeline.csv

# Filter timeline to specific date range
mactime -b /cases/case-2024-001/bodyfile.txt \
   -d 2024-01-15..2024-01-20 > /cases/case-2024-001/incident_timeline.csv

Key Concepts

ConceptDescription
Ingest ModulesAutomated analysis plugins that process data sources upon import
MFT (Master File Table)NTFS metadata structure recording all file entries and attributes
File carvingRecovering files from unallocated space using file signatures
Hash filteringUsing NSRL or custom hash sets to exclude known-good or flag known-bad files
Timeline analysisChronological reconstruction of file system and user activity events
Deleted file recoveryRestoring files whose directory entries are removed but data remains
Keyword indexingFull-text search index built from all file content including slack space
Artifact extractionAutomated parsing of browser, email, registry, and OS-specific artifacts

Tools & Systems

ToolPurpose
AutopsyOpen-source GUI forensic platform for disk image analysis
The Sleuth Kit (TSK)Command-line forensic toolkit underlying Autopsy
flsList files and directories in a disk image including deleted entries
icatExtract file content by inode number from a disk image
mactimeGenerate timeline from TSK bodyfile format
mmlsDisplay partition layout of a disk image
NSRLNIST hash database for identifying known software files
sigfindSearch for file signatures at the sector level

Common Scenarios

Scenario 1: Employee Data Theft Investigation Import the employee workstation image, run all ingest modules, search for company-confidential file names and keywords, examine USB connection artifacts in Recent Activity, check for cloud storage client artifacts, review deleted files for evidence of data staging, generate HTML report for legal team.

Scenario 2: Malware Infection Forensics Add the compromised system image, enable Extension Mismatch and Encryption Detection modules, examine the prefetch directory for execution evidence, search for known malware hashes, build timeline around the infection window, extract suspicious executables for further analysis in a sandbox.

Scenario 3: Child Exploitation Material (CSAM) Investigation Import image with PhotoDNA and Project VIC hash sets enabled, run Picture Analyzer module, hash all image files against known-bad databases, tag and categorize matches by severity, generate law enforcement report with chain of custody documentation.

Scenario 4: Intellectual Property Dispute Import multiple employee disk images as separate data sources in one case, perform keyword searches for proprietary terms and project names, compare file hashes between sources, build timeline showing file access and transfer patterns, export evidence for legal review.

Output Format

Autopsy Case Analysis Summary:
  Case:           CASE-2024-001-Workstation
  Image:          evidence.dd (500GB NTFS)
  Partitions:     2 (System Reserved + Primary)
  Total Files:    245,832
  Deleted Files:  12,456 (recoverable: 8,234)

  Ingest Results:
    Hash Matches (Known Bad):  3 files
    Extension Mismatches:      17 files
    Keyword Hits:              234 across 45 files
    Encrypted Files:           5 containers detected
    EXIF Data Extracted:       1,245 images with metadata

  Tagged Evidence:
    Critical:     12 items
    Supporting:   34 items
    Review:       67 items

  Timeline Events:  1,234,567 entries (filtered to incident window: 892)
  Report:          /cases/case-2024-001/reports/autopsy_report.html
how to use analyzing-disk-image-with-autopsy

How to use analyzing-disk-image-with-autopsy on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add analyzing-disk-image-with-autopsy
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/analyzing-disk-image-with-autopsy

The skills CLI fetches analyzing-disk-image-with-autopsy from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/analyzing-disk-image-with-autopsy

Reload or restart Cursor to activate analyzing-disk-image-with-autopsy. Access the skill through slash commands (e.g., /analyzing-disk-image-with-autopsy) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.642 reviews
  • Anaya Menon· Dec 16, 2024

    analyzing-disk-image-with-autopsy has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Thompson· Dec 12, 2024

    Useful defaults in analyzing-disk-image-with-autopsy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ganesh Mohane· Dec 8, 2024

    analyzing-disk-image-with-autopsy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Dec 8, 2024

    We added analyzing-disk-image-with-autopsy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ama Tandon· Dec 4, 2024

    Solid pick for teams standardizing on skills: analyzing-disk-image-with-autopsy is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 27, 2024

    Keeps context tight: analyzing-disk-image-with-autopsy is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kwame Abbas· Nov 15, 2024

    We added analyzing-disk-image-with-autopsy from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Naina Sharma· Nov 7, 2024

    Useful defaults in analyzing-disk-image-with-autopsy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Min Khanna· Nov 3, 2024

    analyzing-disk-image-with-autopsy has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Meera Sethi· Oct 26, 2024

    analyzing-disk-image-with-autopsy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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