LitterBox

Table of Contents

• Overview
• Analysis Capabilities
• Analysis Engines
• Integrated Tools
• API Reference
• Installation
• Access Methods
• Configuration
• Client Libraries
• Contributing
• Security Advisory
• Acknowledgments
• Interface

Overview

LitterBox provides a controlled sandbox environment designed for security professionals to develop and test payloads. This platform allows red teams to:

• Test evasion techniques against modern detection techniques
• Validate detection signatures before field deployment
• Analyze malware behavior in an isolated environment
• Keep payloads in-house without exposing them to external security vendors
• Ensure payload functionality without triggering production security controls

The platform includes LLM-assisted analysis capabilities through the LitterBoxMCP server, offering advanced analytical insights using natural language processing technology.

Note: While designed primarily for red teams, LitterBox can be equally valuable for blue teams by shifting perspective – using the same tools in their malware analysis workflows.

Analysis Capabilities

Initial Processing

Feature	Description
File Identification	Multiple hashing algorithms (MD5, SHA256)
Entropy Analysis	Detection of encryption and obfuscation
Type Classification	Advanced MIME and file type analysis
Metadata Preservation	Original filename and timestamp tracking

Executable Analysis

For Windows PE files (.exe, .dll, .sys):

• Architecture identification (PE32/PE32+)
• Compilation timestamp verification
• Subsystem classification
• Entry point analysis
• Section enumeration and characterization
• Import/export table mapping

Document Analysis

For Microsoft Office files:

• Macro detection and extraction
• VBA code security analysis
• Hidden content identification
• Obfuscation technique detection

Analysis Engines

Static Analysis

• Industry-standard signature detection
• Binary entropy profiling
• String extraction and classification
• Pattern matching for known indicators

Dynamic Analysis

Available in dual operation modes:

• File Analysis: Focused on submitted samples
• Process Analysis: Targeting running processes by PID

Capabilities include:

• Runtime behavioral monitoring
• Memory region inspection and classification
• Process hollowing detection
• Code injection technique identification
• Sleep pattern analysis
• Windows telemetry collection via ETW

Doppelganger Analysis

Blender Module

Provides system-wide process comparison by:

• Collecting IOCs from active processes
• Comparing process characteristics with submitted payloads
• Identifying behavioral similarities

FuzzyHash Module

Delivers code similarity analysis through:

• Maintained database of known tools and malware
• ssdeep fuzzy hash comparison methodology
• Detailed similarity scoring and reporting

Integrated Tools

Static Analysis Suite

• YARA - Signature detection engine
• CheckPlz - AV detection testing framework
• Stringnalyzer - Advanced string analysis utility

Dynamic Analysis Suite

• YARA Memory - Runtime pattern detection
• PE-Sieve - In-memory malware detection
• Moneta - Memory region IOC analyzer
• Patriot - In-memory stealth technique detection
• RedEdr - ETW telemetry collection
• Hunt-Sleeping-Beacons - C2 beacon analyzer
• Hollows-Hunter - Process hollowing detection

API Reference

File Operations

POST   /upload                    # Upload samples for analysis
GET    /files                     # Retrieve processed file list

Analysis Endpoints

GET    /analyze/static/<hash>     # Execute static analysis
POST   /analyze/dynamic/<hash>    # Perform dynamic file analysis  
POST   /analyze/dynamic/<pid>     # Conduct process analysis

Doppelganger API

# Blender Module
GET    /doppelganger?type=blender               # Retrieve latest scan results
GET    /doppelganger?type=blender&hash=<hash>   # Compare process IOCs with payload  
POST   /doppelganger                            # Execute system scan with {"type": "blender", "operation": "scan"}

# FuzzyHash Module
GET    /doppelganger?type=fuzzy                 # Retrieve fuzzy analysis statistics
GET    /doppelganger?type=fuzzy&hash=<hash>     # Execute fuzzy hash analysis
POST   /doppelganger                            # Generate database with {"type": "fuzzy", "operation": "create_db", "folder_path": "C:\path\to\folder"}

Results Retrieval (JSON)

GET    /api/results/<hash>/info      # Retrieve file metadata
GET    /api/results/<hash>/static    # Access static analysis results
GET    /api/results/<hash>/dynamic   # Obtain dynamic analysis data
GET    /api/results/<pid>/dynamic    # Retrieve process analysis data

HTML Report Generation

GET    /api/report/          # Generate comprehensive HTML report (target = hash or pid)
GET    /api/report/?download=true  # Download report as file attachment
GET    /report/              # Download report directly (redirects to api with download=true)

Web Interface Results

GET    /results/<hash>/info      # View file information
GET    /results/<hash>/static    # Access static analysis reports
GET    /results/<hash>/dynamic   # View dynamic analysis reports
GET    /results/<pid>/dynamic    # Access process analysis reports

System Management

GET    /health                   # System health verification
POST   /cleanup                  # Remove analysis artifacts
POST   /validate/<pid>           # Verify process accessibility
DELETE /file/<hash>              # Remove specific analysis

Installation

System Requirements

• Windows operating system (Linux not supported)
• Python 3.11 or higher
• Administrator privileges

Deployment Process

1. Clone the repository:

git clone https://github.com/BlackSnufkin/LitterBox.git
cd LitterBox

2. Configure environment:

python -m venv venv
.\venv\Scripts\Activate.ps1
pip install -r requirements.txt

Operation

Standard operation:

python litterbox.py

Diagnostic mode:

python litterbox.py --debug

Access Methods

LitterBox offers three access interfaces:

• Web UI: Browser-based interface at http://127.0.0.1:1337
• API Access: Programmatic integration via Python client
• LLM Integration: AI agent interaction through MCP server

For API access, see the Client Libraries section.

Configuration

All settings are stored in config/config.yml. Edit this file to:

• Change server settings (host/port)
• Set allowed file types
• Configure analysis tools
• Adjust timeouts

Client Libraries

For programmatic access to LitterBox, use the GrumpyCats package:

GrumpyCats Documentation

The package includes:

• grumpycat.py: Dual-purpose tool that functions as:

  • Standalone CLI utility for direct server interaction
  • Python library for integrating LitterBox capabilities into custom tools

• LitterBoxMCP.py: Specialized server component that:

  • Wraps the GrumpyCat library functionality
  • Enables LLM agents to interact with the LitterBox analysis platform
  • Provides natural language interfaces to malware analysis workflows

Contributing

Development contributions should be conducted in feature branches on personal forks. For detailed contribution guidelines, refer to: CONTRIBUTING.md

Security Advisory

• DEVELOPMENT USE ONLY: This platform is designed exclusively for testing environments. Production deployment presents significant security risks.
• ISOLATION REQUIRED: Execute only in isolated virtual machines or dedicated testing environments.
• WARRANTY DISCLAIMER: Provided without guarantees; use at your own risk.
• LEGAL COMPLIANCE: Users are responsible for ensuring all usage complies with applicable laws and regulations.

Acknowledgments

This project incorporates technologies from the following contributors:

• Elastic Security
• hasherezade
• Forrest Orr
• rasta-mouse
• thefLink
• joe-desimone
• dobin
• mr.d0x

Interface