PayloadPDF

A Python-based steganography tool that enables hiding and extracting secret data within PDF files without altering their visual appearance or functionality using advanced PDF structure manipulation techniques.

🔍 Overview

PayloadPDF implements sophisticated steganographic methods to conceal payloads within PDF documents. The tool employs the orphan object technique as its core method, creating unreferenced PDF objects that remain invisible to standard readers while preserving embedded data.

✨ Features

• Payload Injection: Embed executables, scripts, or binary data using inject.py
• Payload Extraction: Retrieve hidden content using extract.py
• Encryption Support: Optional payload encryption via cryptography module (AES-256)
• Format Conversion: PDF manipulation utilities in converter module
• Cross-platform: Windows, macOS, and Linux compatibility

🏗️ Architecture

The tool follows a modular design with specialized components: The tool follows a modular design with specialized components:

PayloadPDF/

• converter/ # PDF format conversion utilities
• cryptography/ # Encryption/decryption modules
• extractor/ # Payload extraction logic
• injector/ # Payload embedding mechanisms
• logger/ # Logging and debugging tools
• extract.py # Main extraction script
• inject.py # Main injection script
• parser.py # Argument parser

🔬 Steganographic Method: Orphan Objects

The core technique creates orphan objects - PDF objects that exist in the file structure but are not referenced in the cross-reference table (xref). This approach offers several advantages:

• Invisibility: PDF readers ignore unreferenced objects completely
• Preservation: Original PDF functionality remains intact
• Detection Resistance: Standard analysis tools miss orphaned content
• Capacity: Can store substantial payloads without size limitations

Technical Implementation

Standard PDF:

1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj
2 0 obj << /Type /Pages /Kids [3 0 R] >> endobj
3 0 obj << /Type /Page ... >> endobj

Modified with Orphan:

1 0 obj << /Type /Catalog /Pages 2 0 R >> endobj
2 0 obj << /Type /Pages /Kids [3 0 R] >> endobj
4 0 obj << /Payload [HIDDEN_DATA encoded in base64] >> endobj ← ORPHAN
3 0 obj << /Type /Page ... >> endobj

xref
0 4
0000000000 65535 f
0000000010 00000 n ← obj 1
0000000156 00000 n ← obj 2 + 4 (ORPHAN) Offset
0000000223 00000 n ← obj 3

📦 Installation

git clone https://github.com/0x00spor3/PayloadPDF
cd PayloadPDF
pip install -r requirements.txt

🚀 Usage

Inject Payload

File

python3 inject.py --i input.pdf --o output.pdf --m file --f test.exe --e Password 

Text

python3 inject.py --i input.pdf --o output.pdf --m text --t "Secret Text" --e Password 

Extract Payload

File

python3 extract.py --i output.pdf --m file --e Password 

Text

python3 extract.py --i output.pdf --m text --e Password 

🎯 Use Cases

• Security Research: PDF malware analysis and detection evasion studies
• Red Team Operations: Covert payload delivery mechanisms
• Digital Forensics: Advanced data hiding investigations
• Penetration Testing: Bypassing content filtering systems
• Academic Research: Steganography algorithm development

🛡️ Detection Evasion Capabilities

• Visual Integrity: No changes to PDF appearance or layout
• Functional Preservation: All original PDF features remain operational
• Metadata Consistency: File properties appear unchanged
• Scanner Bypass: Evades standard antivirus and content filters
• Forensic Resistance: Requires specialized analysis to detect

📄 License

MIT License - See LICENSE file for full details.