Adversarial ML security

Your model is accurate.
That doesn't mean it's safe.

Ekushield measures how easily your classifiers, LLM guardrails, and vision models can be pushed into the wrong answer — with imperceptible input changes an attacker controls, not you.

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1,240+Models scanned
38Attack techniques
<90sTo first report
LIVE DEMO — fgsm_mechanism.js
Original classification
BENIGN
After perturbation
BENIGN
Model confidence after perturbation
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A simplified 2D walkthrough of the FGSM mechanism — the same gradient-sign step used against real image and tabular classifiers. Drag the slider.

Detect

Attack simulation library

Run FGSM, PGD, boundary-attack, and data-poisoning simulations against your model without exposing it to the public internet.

Measure

Robustness scoring

Get a critical-epsilon score: the smallest perturbation budget that flips your model's decision, benchmarked against your industry.

Fix

Hardening recommendations

Actionable guidance — adversarial training, input sanitization, gradient masking detection, confidence-score throttling — ranked by impact.

Vulnerability register

The failure modes we test for

A working sample from the register every scan checks against. Full catalogue and CVE-style references live in the docs.

EL-014 Evasion via gradient-sign perturbation Small, humanly-imperceptible input changes crafted from the model's own gradient flip the predicted label. High
EL-021 Confidence-score oracle exposure Raw probability scores returned by an API let attackers estimate gradients without white-box access. High
EL-033 Training-data poisoning susceptibility Insufficient provenance checks on retraining data allow slow, targeted drift of decision boundaries. Medium
EL-047 Prompt-injection guardrail bypass Adversarial suffixes and encoding tricks that route around LLM safety classifiers. Medium
EL-058 Model extraction via query budget Unthrottled prediction APIs allow attackers to approximate your model with enough queries. Low

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