analyzing-disk-image-with-autopsy

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

4,032 stars

Best use case

analyzing-disk-image-with-autopsy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using analyzing-disk-image-with-autopsy should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/analyzing-disk-image-with-autopsy/SKILL.md --create-dirs "https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/analyzing-disk-image-with-autopsy/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/analyzing-disk-image-with-autopsy/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How analyzing-disk-image-with-autopsy Compares

Feature / Agentanalyzing-disk-image-with-autopsyStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

```bash
# 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)
```

```bash
# 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
```

```bash
# 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

```bash
# 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
```

```bash
# 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/
```

```bash
# 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

securing-container-registry-images

4032
from mukul975/Anthropic-Cybersecurity-Skills

Securing container registry images by implementing vulnerability scanning with Trivy and Grype, enforcing image signing with Cosign and Sigstore, configuring registry access controls, and building CI/CD pipelines that prevent deploying unscanned or unsigned images.

scanning-docker-images-with-trivy

4032
from mukul975/Anthropic-Cybersecurity-Skills

Trivy is a comprehensive open-source vulnerability scanner by Aqua Security that detects vulnerabilities in OS packages, language-specific dependencies, misconfigurations, secrets, and license violati

scanning-container-images-with-grype

4032
from mukul975/Anthropic-Cybersecurity-Skills

Scan container images for known vulnerabilities using Anchore Grype with SBOM-based matching and configurable severity thresholds.

performing-disk-forensics-investigation

4032
from mukul975/Anthropic-Cybersecurity-Skills

Conducts disk forensics investigations using forensic imaging, file system analysis, artifact recovery, and timeline reconstruction to support incident response cases. Utilizes tools such as FTK Imager, Autopsy, and The Sleuth Kit for evidence acquisition, deleted file recovery, and artifact examination. Activates for requests involving disk forensics, hard drive analysis, forensic imaging, file recovery, evidence acquisition, or digital forensic investigation.

performing-container-image-hardening

4032
from mukul975/Anthropic-Cybersecurity-Skills

This skill covers hardening container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure production-ready images.

implementing-image-provenance-verification-with-cosign

4032
from mukul975/Anthropic-Cybersecurity-Skills

Sign and verify container image provenance using Sigstore Cosign with keyless OIDC-based signing, attestations, and Kubernetes admission enforcement.

implementing-disk-encryption-with-bitlocker

4032
from mukul975/Anthropic-Cybersecurity-Skills

Implements full disk encryption using Microsoft BitLocker on Windows endpoints to protect data at rest from unauthorized access in case of device loss or theft. Use when deploying encryption for compliance requirements, securing mobile workstations, or implementing data protection controls across the enterprise. Activates for requests involving BitLocker encryption, disk encryption, TPM configuration, or data-at-rest protection.

implementing-container-image-minimal-base-with-distroless

4032
from mukul975/Anthropic-Cybersecurity-Skills

Reduce container attack surface by building application images on Google distroless base images that contain only the application runtime with no shell, package manager, or unnecessary OS utilities.

analyzing-windows-shellbag-artifacts

4032
from mukul975/Anthropic-Cybersecurity-Skills

Analyze Windows Shellbag registry artifacts to reconstruct folder browsing activity, detect access to removable media and network shares, and establish user interaction with directories even after deletion using SBECmd and ShellBags Explorer.

analyzing-windows-registry-for-artifacts

4032
from mukul975/Anthropic-Cybersecurity-Skills

Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.

analyzing-windows-prefetch-with-python

4032
from mukul975/Anthropic-Cybersecurity-Skills

Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.

analyzing-windows-lnk-files-for-artifacts

4032
from mukul975/Anthropic-Cybersecurity-Skills

Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.