Harden Your AI
Against Invisible Attacks
Even the most advanced AI models can be fooled. Perturb helps you discover vulnerabilities before attackers do.
AI Models Are More Fragile Than You Think...
A single pixel changed. A whisper of noise. That’s all it takes to make state-of-the-art models confidently misclassify, leak sensitive signals, or behave unpredictably in real-world environments. These aren’t rare edge cases, they expose fundamental weaknesses in how neural networks interpret data.
To users, everything looks fine. To your model, everything breaks. A stop sign becomes a speed limit. A trusted system becomes unreliable. And the most dangerous part, these failures often stay hidden until your model is already in production. Perturb makes these invisible threats visible, using a decentralized network to simulate real-world attacks and uncover vulnerabilities before they impact users, trust, or revenue.
Be Prepared With Adversarial Examples!
As mentioned earlier, even the best AI models can be fooled. Adversarial examples are malicious inputs created to deceive your AI that can lead your system to make critical mistakes.
What Is an Adversarial Example?
Adversarial examples are inputs designed to fool AI models. They can work on any kind of data, such as images, text or audio. They include perturbations that push the model to make incorrect predictions.
For example, an image of a stop sign might be modified so that an AI sees a speed limit sign instead. In natural language processing, a small word tweak might cause a sentiment classifier to flip from "positive" to "negative." These attacks exploit the fact that many AI models learn patterns that are statistically useful but semantically fragile.
Can You Spot The Difference?
Nothing you can see. Everything the model relies on. A carefully crafted perturbation shifts the model’s internal perception just enough to trigger a completely different prediction, while the image appears identical to humans.
Original Image
A photo of a dog correctly classified as a Rhodesian Ridgeback by EfficientNet-B5 Model.
+ Imperceptible Perturbation
The Famous FGSM Noice. The attack adjusts the input data to maximize the loss based on the same backpropagated gradients. In other words, the attack uses the gradient of the loss w.r.t the input data, then adjusts the input data to maximize the loss.
Adversarial Image
The same image, with a tiny, imperceptible perturbation added—no visible difference to the human eye.
Model Prediction Changes To:
A New Layer Of Defense
Everything you need to evaluate, harden, and ship AI you can trust. Not from a single vendor, but from a global network of adversarial miners incentivized to find real vulnerabilities in your models before attackers do.
Decentralized Network
No single point of failure. A global subnet of miners continuously probes your models.
Incentivized Attackers
Bittensor rewards aligned with finding real, exploitable vulnerabilities, not synthetic ones.
Continuous Hardening
Your models are evaluated 24/7 as new attack techniques emerge across the network.
Automated at Scale
From a single classifier to massive multi-modal LLMs, Perturb scales with your stack.
Real-World Simulation
Black-box, white-box, and transfer attacks that mirror how adversaries operate in production.
Actionable Insights
Robustness scores, vulnerability heatmaps, and hardening datasets ready for retraining.
Frequently Asked Questions
Everything you need to know about adversarial robustness testing and how Perturb protects your models.
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