5 AI Vulnerabilities You Must Know in 2023

Artificial Intelligence (AI) plays a critical role in the changing landscape of many industries. Cyber Security is also affected by the emergence of AI in the current scenario. Cyber Security is a huge field, and new security issues raised because of AI.

This blog discusses 5 AI vulnerabilities that you must know in 2023.

Data Poisoning

Any AI system is fed with training data when training the system initially. If the attacker poisons the training data, the AI system learns the wrong patterns. As a result, the whole AI system behaves erroneously and does not work as expected.

For example, a Firewall with AI features is installed in a network to detect cyber attacks on the network. Initially, the Firewall learned the behavior of traffic by deploying it in the network. If an attacker sends malicious traffic in the beginning, the Firewall understands that the malicious packets are the expected ones. When it is operation stage, if the firewall again encounters the same type of malicious packets, it will allow it to learn that the receiving of malicious packets is normal.

Data Evasion

This type of attack is quite common against AI systems. In this type of attack, there will be changes in some training data, which result in a huge change in the assessment methodology of the model. Although, if humans consume the same data, there will be no change in the assessment.

Assume an AI system is deployed to determine the animal. If some pixels are changed in the image, the AI system is not able to identify the animal correctly. However, human eye can easily identify animals as elephants without any effort.

Membership Inference

Membership inference is an attack that targets machine learning models, including those used in AI applications. It involves an attacker attempting to determine whether a specific data point was part of the training dataset used to build a machine learning model.

This attack has important implications (e.g. medical data) for the privacy of individuals whose data is included in the training dataset.

Model Extraction

AI system operates on the principle of defined algorithms and processes. If an attacker knows the internal design of the AI system, this will pose a huge risk to the AI system. As an attacker is able to replicate the same model, by making some malicious changes in the system.

In this type of attack, the attacker easily fools the AI system as he/she knows all the design and implementation details of the AI system.

Model Inversion

This type of attack works on the principle of reverse engineering. Here, the attacker changes the output and tries to get private information.

In summary, model inversion is an attack that focuses on the reverse engineering of a machine learning model and helps in uncovering sensitive data used in its training.

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Disclaimer: This tutorial is for educational purpose only. Individual is solely responsible for any illegal act.

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