analyzing-disk-image-with-autopsy▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Perform comprehensive forensic analysis of disk images using Autopsy to recover files, examine artifacts, and build investigation timelines.
| 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
| Concept | Description |
|---|---|
| Ingest Modules | Automated analysis plugins that process data sources upon import |
| MFT (Master File Table) | NTFS metadata structure recording all file entries and attributes |
| File carving | Recovering files from unallocated space using file signatures |
| Hash filtering | Using NSRL or custom hash sets to exclude known-good or flag known-bad files |
| Timeline analysis | Chronological reconstruction of file system and user activity events |
| Deleted file recovery | Restoring files whose directory entries are removed but data remains |
| Keyword indexing | Full-text search index built from all file content including slack space |
| Artifact extraction | Automated parsing of browser, email, registry, and OS-specific artifacts |
Tools & Systems
| Tool | Purpose |
|---|---|
| Autopsy | Open-source GUI forensic platform for disk image analysis |
| The Sleuth Kit (TSK) | Command-line forensic toolkit underlying Autopsy |
| fls | List files and directories in a disk image including deleted entries |
| icat | Extract file content by inode number from a disk image |
| mactime | Generate timeline from TSK bodyfile format |
| mmls | Display partition layout of a disk image |
| NSRL | NIST hash database for identifying known software files |
| sigfind | Search 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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches analyzing-disk-image-with-autopsy from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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.
List & Monetize Your Skill
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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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★42 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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