Think You Can You Attack Our Neural Networks? Prove it

At Matroid, we believe the best engineers aren’t just great at writing code—they think differently, push boundaries, and challenge the impossible.

That’s why our Founder & CEO, Reza Zadeh, has issued a challenge:

“Find an adversarial example for a specific neural network within 4 hours, and we’ll put a 90th-percentile market offer on the table.”

Interested? Hundreds of Engineers from Google, Meta, Microsoft, Amazon, and more have all tried—and failed.

What are Adversarial Attacks?

An adversarial attack is a technique for fooling machine learning models, particularly deep neural networks. It subtly manipulates input data to cause the model to make incorrect predictions. These attacks exploit weaknesses in how AI systems process information.

How Adversarial Attacks Work

In Computer Vision, adversarial attacks exploit detectors (trained neural networks) by making small, carefully crafted changes to an image or video that a detector will process, confusing or disrupting the result or detection.

Why We Care

We don’t just build AI—we push its limits. Our computer vision detectors power some of the world’s most demanding industries, from manufacturing and aerospace to National Security. Protecting these models is institutionally critical.

Think You’re Up for It?

If you thrive on breaking things to improve them and live for the thrill of solving problems no one else can, we want you at Matroid.
The challenge remains unsolved. Think you can crack it? Reach out to our CEO via [email protected] with the Subject: Matroid Adversarial Challenge

Update

March 2025: Congratulations to Alexandra Puchko for being the first to successfully attack this model! She beat out hundreds of other scientists and researchers. Well done! Matroid’s model has already been updated to defend against her finding.