5 AI Vulnerabilities You Must Know in 2024

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 2024.

5 AI Vulnerabilities You Must Know in 2024

Attack Type	Description
Data Poisoning	Manipulation of training data to compromise model performance or integrity during training or inference.
Data Evasion	Crafting input data to mislead the model, causing it to make incorrect predictions during inference.
Membership Inference	Inferring whether a specific data point was part of the training set, potentially compromising privacy.
Model Extraction	Extracting details or the entire architecture of a machine learning model by querying it strategically.
Model Inversion	Inferring sensitive information about training data by exploiting the outputs or responses of a model.

Data Poisoning

Any AI system is fed with training data when readying 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.

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. An attacker can 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/sensitive information.

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

Conclusion

Adversarial attacks, like data poisoning and model extraction, can harm machine learning models. To protect them, we need strong models and clean data. Researchers are working to make models more resilient, keeping AI systems safe and reliable in real-world situations.