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analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports.

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-disk-image-with-autopsy

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

98/100

Category

Security

Supported Platforms

Universal

Our assessment of analyzing-disk-image-with-autopsy

analyzing-disk-image-with-autopsy scores 98/100 on our quality scale, 23rd of 461 Security skills we index (top 5%).

Its SKILL.md is 11 KB long, well organised into 50 sections with 11 code examples: a thorough specification that gives an agent plenty to work with.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so analyzing-disk-image-with-autopsy is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

analyzing-disk-image-with-autopsy compared with similar skills

All 4 of these similar skills score higher than analyzing-disk-image-with-autopsy; compare them before choosing.

SkillScoreStarsUpdatedFormat
analyzing-disk-image-with-autopsy (this skill)by mukul9759833.3k25d agoSKILL.md
Agent-Reachby Panniantong10085.4k9d agoCLAUDE.md
LocalAIby mudler10049.3ktodayMCP Server
algorithmic-artby anthropics100177.9k2d agoSKILL.md
pptxby anthropics100177.9k2d agoSKILL.md

Frequently asked questions

How do I install analyzing-disk-image-with-autopsy?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-disk-image-with-autopsy. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-disk-image-with-autopsy work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-disk-image-with-autopsy safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-disk-image-with-autopsy still maintained?
The repository was last updated 25 days ago, so analyzing-disk-image-with-autopsy is actively maintained.

name: analyzing-disk-image-with-autopsy description: Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence. 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-03
  • DE.AE-02
  • RS.MA-01 mitre_attack:
  • T1005
  • T1074.001
  • T1070.004
  • T1083

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

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions