Data Poisoning: The Hidden Threat to AI Models

As ongoing developments in the realms of artificial intelligence and machine learning take place at a dynamic rate, yet another new form of attack is emerging, one which can topple all those systems we use today without much ado: data poisoning. This type of attack involves tampering with data used by AI models in training to make them malfunction, often undetectably. The issue came to light when recently, more than 100 malicious models were uncovered on the popular repository for AI, Hugging Face, by a software management company, JFrog.

What is Data Poisoning?

Data poisoning is an attack method on AI models by corrupting the data used for its training. In other words, the intent is to have the model make inappropriate predictions or choices. Besides, unlike traditional hacking, it doesn’t require access to the system; therefore, data poisoning manipulates input data either before the deployment of an AI model or after the deployment of the AI model, and that makes it very difficult to detect.

One attack happens at the training phase when an attacker manages to inject malicious data into any AI model. Yet another attack happens post-deployment when poisoned data is fed to the AI; it yields wrong outputs. Both kinds of attacks remain hardly detectable and cause damage to the AI system in the long run.

According to research by JFrog, investigators found a number of suspicious models uploaded to Hugging Face,

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