Secure & Audit
Adversarial Robustness Probe
Seven attack types against NLP and vision models, measuring prediction flip rates with shareable HTML reports.
The problem
Teams have no systematic way to measure how often a model's predictions flip under adversarial input before shipping it.
- A model ships, and the first adversarial input it sees in production is also the first one anyone tested.
- Two candidate models both look accurate; only one holds up when the input gets noisy.
- "It seemed robust" isn't something you can put in a release checklist.
What NEO built
NEO built a stress-testing framework running seven attack types (typos, paraphrasing, FGSM, noise injection) against NLP and vision models, scoring flip rate and grading A-F.
The result
7 attack types, A–F grading
Produces interpretable, shareable robustness grades and reports usable as a CI/CD deployment gate.
From the blog · 8 min
Adversarial Robustness Probe: Stress-Testing NLP and Vision Models Before They Ship
NEO built Adversarial Probe, a framework that applies seven attack types to NLP and vision models, measures prediction flip rates, and generates shareable HTML reports for security, compliance, and model selection.
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Stress-test my model against common adversarial attacks (typos, paraphrasing, noise injection) and grade how often its predictions flip.
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