MirageLabs

Robustness testing for the AI systems the world runs on. We find where perception, detection, and autonomy break.

The world is moving to secure LLMs. But the models steering perception, detection, and autonomy often go untested. We test those.

Any
attack method
Any
modality
Any
model architecture

Explain. Exploit. Protect.

01

Explain

See how a model decides, and where it leans on the wrong signal.

SaliencyAttributionLayer analysis
02

Exploit

The Mirage platform attacks your model like a malicious cyber actor would. Gradient, physical, black box. Prove every failure.

Adversarial patchesEvasion attacksModel extraction
03

Protect

Turn failures into defenses that hold, from adversarial training to certified robustness.

Adversarial trainingCertified robustnessDetection

A few bits is all it takes

A perturbation too small to notice flips a confident prediction. It works on pixels, on sound, on network traffic. It never shows up in an accuracy score. It is the first thing we look for.

VisionImage classifier
Input
Aircraft carrierResNet
+ δ
Prediction
Aircraft carrier94.8%
AudioWav2Vec2 speech recognition
Input audio
“play the next song”
+ δ
Transcription
“play the next song”
CybersecurityAnomaly detector evasion
Network flow
Intrusion flagged
+ δ
Detector
Intrusion flagged

Every surface that can be fooled

Watch it break a live model

Real attacks across vision, audio, text, and network models. No setup. See exactly where a model fails, and by how much.

Talk to us

Book a demo, or tell us where you need to be more resilient.

We usually reply within 24 hours.