Attack On AI Models: What You Need to Know!

Artificial Intelligence (AI) powers a wide range of modern technologies — from autonomous vehicles to facial recognition systems. Every AI application relies on a carefully designed and trained model. But did you know these models can be targeted by malicious attacks? In this article, we explore the risks facing AI models and how to defend against them.

Understanding AI Models and Their Vulnerability

When deployed, an AI model is stored in a file that contains its structure and parameters — neural layers, activation functions, weights and biases — enabling the model to produce the expected results.

Analogy: Your AI Model Is Like a House

Picture this: you’re building a house. You have blueprints detailing where to place each wall, window, and which material to use.
In the same way, an AI model file specifies — like a “blueprint” — how the model works.

But what happens if this file isn’t properly protected? Let’s continue with our example:

Now imagine that your house has no fence or security system. Anyone could enter, examine the interior layout, copy your blueprints and even steal valuable items. The same goes for unprotected AI model files — they can be accessed, copied, and reverse engineered by malicious actors.

In a previous article, we demonstrated how easy it is, in practice, to extract this type of file from an AI model:
SSTIC2023 » Présentation » Your Mind is Mine: How to Automatically Steal DL Models From Android Apps — Marie Paindavoine, Maxence Despres, Mohamed Sabt

Why Protecting Your AI Models Is Important

It is crucial to safeguard your AI models in today’s competitive digital landscape.
Failing to secure AI models opens the door to a range of threats:

• Reverse Engineering: Competitors or attackers can replicate your intellectual property without investing in R&D.
• Model Inversion Attacks: Attackers can reconstruct training data from the model, posing serious privacy risks.
• Adversarial Examples: Small, crafted input modifications can manipulate the model’s outputs—especially dangerous in fields like autonomous driving or finance.
• White-Box Attacks: If the attacker gains full access to the model, they can uncover its architecture and logic, making it easier to craft targeted exploits.

Business Impact

• Loss of competitive advantage
• Exposure of sensitive user data
• Compromised brand trust and regulatory compliance
  
  

How to Protect AI Models: Skyld’s Approach

At Skyld, we specialize in securing AI models at rest and during execution.
Our SDK prevents the extraction of your model’s sensitive parameters — such as weights and biases.

Your model will be safeguarded against:

• Reverse engineering
• Inference attacks
• Runtime tampering

All while ensuring runtime integrity during deployment.

Secure your “AI house” with Skyld — protect what powers your product, brand, and future.
Contact us today to learn how our technology can help fortify your AI models.