PlatformCompanyPricingBlogsNexula Labs
Start
Adversarial Testing

Test Your Models Against Real-World Attacks

Automated adversarial testing to find vulnerabilities before attackers do

Start Testing FreeRequest Demo

Attack Vectors We Test

🎯

Evasion Attacks

Crafted inputs that fool your model at inference time

FGSMPGDC&WDeepFool
🦠

Poisoning Attacks

Malicious training data that corrupts model behavior

Label flippingBackdoor injectionClean-label attacks
🔍

Model Extraction

Techniques to steal model functionality via queries

Query-based extractionSide-channel attacks
🕵️

Membership Inference

Determine if data was used in training

Shadow modelsConfidence-based attacks
🔓

Model Inversion

Reconstruct training data from model outputs

Gradient-based inversionGAN-based attacks
💉

Prompt Injection

Manipulate LLM behavior through crafted prompts

Direct injectionIndirect injectionJailbreaks

Supported Model Types

👁️

Computer Vision

Image classifiers, object detection, segmentation

📝

NLP Models

Text classifiers, NER, sentiment analysis

🤖

LLMs

GPT, Llama, Claude, custom fine-tunes

🎯

Tabular Models

XGBoost, LightGBM, Random Forest

🔊

Audio Models

Speech recognition, audio classification

📊

Time Series

Forecasting, anomaly detection

🎮

RL Models

Reinforcement learning policies

🔗

Multimodal

Vision-language, CLIP-based models

Automated Testing Pipeline

01

Connect Model

API endpoint, model file, or inference function

02

Select Attacks

Choose attack types or run full suite

03

Generate Tests

AI creates adversarial inputs for your model

04

Get Report

Robustness score, vulnerabilities, fixes

Robustness Report Metrics

Attack Success Rate

Percentage of adversarial inputs that fool your model

Good: < 5%Poor: > 20%

Perturbation Budget

Minimum noise needed to cause misclassification

Good: > 0.3Poor: < 0.1

Robustness Score

Overall model resilience (0-100)

Good: > 80Poor: < 50

LLM Security Testing

Prompt Injection Testing

Test resistance to direct and indirect prompt injection attacks

• System prompt extraction
• Instruction override
• Context manipulation

Jailbreak Detection

Evaluate model against known and novel jailbreak techniques

• DAN prompts
• Roleplay attacks
• Encoding bypasses

Data Leakage Testing

Check if model reveals training data or sensitive information

• PII extraction
• Training data inference
• System prompt leakage

Output Manipulation

Test for ability to generate harmful or unintended outputs

• Toxicity generation
• Misinformation
• Code injection

CI/CD Integration

Run adversarial tests automatically on every model update. Block deployments that fail robustness thresholds.

  • ✓ GitHub Actions workflow
  • ✓ GitLab CI/CD pipeline
  • ✓ MLflow model registry hooks
  • ✓ Kubeflow pipeline step
  • ✓ Custom webhook triggers
  • ✓ Scheduled robustness audits
# GitHub Action
- name: Adversarial Testing
uses: nexula/adversarial-test@v1
with:
model: ./model.pt
attacks: [fgsm, pgd, c&w]
threshold: 80
# Blocks if score < 80

Test Your Model's Robustness

Run your first adversarial test in under 5 minutes. Free tier includes 100 test runs/month.

Start Free TrialTalk to Security Expert