What are the most common AI attacks?

AI Attacks and Adversarial AI in Machine Learning

Today, it’s incredibly common for developers to use AI in their daily workflows. In fact, 92% of developers say they’re already using AI-coding tools in their work. While these tools offer benefits — namely speed — they can also introduce new threats and vulnerabilities. Just as bad actors consistently evolve their techniques to exploit machine learning and AI, organizations need to evolve their cybersecurity.  

To protect your organization from AI attacks and adversarial AI, it’s important to understand what adversarial AI in machine learning is, how attacks work, and ways to detect it within your systems. 

What is adversarial AI? 

When looking at machine learning and AI exploitations, adversarial AI, also known as adversarial machine learning, is when bad actors attempt to alter machine learning systems and AI models by manipulating or deceiving them through data or inputs. Typically, adversarial AI attacks take advantage of AI’s logic and decision-making processes to create malicious outputs.

As adversarial AI is designed to be subtle and undetectable, users often can’t tell when the model has been exploited or compromised.