rednblue 2.4.0

Adversarial security testing CLI for AI models

RednBlue CLI v2.4.0

Zero-Knowledge Adversarial Security Testing for AI Models

RednBlue CLI is a command-line tool for testing the adversarial robustness of machine learning models. Run security assessments locally — your model never leaves your infrastructure.

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Zero-Knowledge Adversarial Security Testing

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Features

• Zero-Knowledge Protocol — Model weights and data never leave your infrastructure
• Image Classifiers — Test ResNet, VGG, EfficientNet, and custom architectures
• YOLO Detection — Full support for YOLOv5, YOLOv8, YOLOv10, YOLOv11
• Tier-Based Testing — Freelancer (quick scan) and Enterprise (comprehensive)
• Encrypted Submission — AES-256 encrypted results with HMAC-SHA256 signing
• Multi-Jurisdiction Compliance — EU AI Act, NIST AI RMF, ISO/IEC 42001, UK DSIT, Canada AIDA, Singapore MAIGF

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Installation

# Clone the repository
git clone https://github.com/mahdidrm/RednBlue_CLI.git
cd RednBlue_CLI

# Install in development mode
pip install -e .

# Verify installation
rnb

Requirements

• Python 3.8+
• PyTorch 2.0+
• CUDA (optional, for GPU acceleration)

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Quick Start

1. Set your token

# Windows
set RNB_TOKEN=RB-XXXXXX-YYYYYY

# Linux/Mac
export RNB_TOKEN=RB-XXXXXX-YYYYYY

2. Run a security assessment

Image Classifier:

rnb preview --model resnet50.pth --input ./test_images --model-type classifier

YOLO Detection Model:

rnb preview --model yolov10n.pt --input ./test_images --model-type yolo

3. Submit for certification

rnb preview --model yolov10n.pt --input ./images --model-type yolo --submit

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Commands

Command	Description
rnb	Show welcome banner and quick start
rnb preview --help	Run adversarial attacks
rnb status	Check token validity and tier
rnb optimize-epsilon	Optimize epsilon values (Enterprise)
rnb test-llm	Test LLM models (Enterprise)

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Assessment Dimensions

Classifier Models

Dimension	Description
Noise Resilience	Stability under sensor noise and interference
Spatial Consistency	Robustness to spatial feature shifts
Universal Pattern Defense	Resistance to universal perturbation patterns
Feature Stability	Internal representation integrity
Confidence Calibration	Prediction reliability accuracy
Iterative Stress Tolerance	Defense against sustained pressure
Optimization Attack Defense	Resistance to optimized adversarial inputs
Deep Perturbation Resistance	Resilience against deep layer perturbations

YOLO Detection Models

Dimension	Description
Noise Resilience	Stability under sensor noise
Input Perturbation Defense	Resistance to subtle input modifications
Iterative Stress Tolerance	Defense against multi-step attacks
Detection Consistency	Reliable detection under varying conditions
Targeted Evasion Defense	Resistance to deliberate misclassification
Object Persistence	Maintains detections under perturbations
Multi-Object Stability	Accuracy in crowded scenes
Black-Box Resilience	Defense without model access
Query-Limited Defense	Resistance to low-query probing

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Tier Comparison

Feature	Freelancer	Enterprise
Classifier Attacks	5	8
YOLO Attacks	4	9
Epsilon Values	2	4
Total Scenarios	~10-20	~30-70
LLM Testing	❌	✅
Epsilon Optimization	❌	✅

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Output Example

============================================================
  RednBlue Security Preview — YOLO Detection
============================================================
  Attacks run    : 21
  Successful hits: 0/21 (0%)
  Robustness rate: 100%
  Estimated Grade: GOLD

  ⚠️  This is a preview only
  → Visit: https://rednblue.io/checkout
  → Re-run with: rnb preview --model-type yolo --submit

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Certification Grades

Grade	Score	Meaning
🥇 GOLD	≥90%	Excellent robustness, deployment ready
🥈 SILVER	≥75%	Good robustness, minor improvements recommended
🥉 BRONZE	≥50%	Moderate robustness, improvements needed

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Architecture

┌─────────────────────────────────────────────────────────┐
│                    Your Infrastructure                   │
│  ┌─────────┐    ┌─────────┐    ┌─────────────────────┐  │
│  │  Model  │───▶│   CLI   │───▶│  Encrypted Results  │  │
│  └─────────┘    └─────────┘    └──────────┬──────────┘  │
└───────────────────────────────────────────┼─────────────┘
                                            │ AES-256
                                            ▼
                              ┌─────────────────────────┐
                              │   RednBlue Platform     │
                              │  dashboard.rednblue.io  │
                              └─────────────────────────┘

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Links

• Platform: https://dashboard.rednblue.io
• Documentation: https://docs.rednblue.ai
• Website: https://rednblue.io

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Authors

• Dr. Mahdi Deramgozin — Chief AI Officer
• Dr. Saeid Samizade — Chief Technology Officer

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License

Proprietary — RednBlue SAS © 2026

Made in France 🇫🇷