ReskLayer

Advanced Prompt Injection Detection

ModernBERT with DiffTransformer Attention

ReskLayer is a cutting-edge solution for detecting malicious prompt injections in AI systems. Our custom ModernBERT model leverages DiffTransformer attention mechanisms and Ettin's three-phase training recipe to provide robust protection against sophisticated attacks like DAN (Do Anything Now).

Model Specifications

                Model Configuration
                
Model: ModernBERT with DiffTransformer
Max Sequence Length: 8192 tokens
Attention: Differentiable attention mechanism
Training: Ettin three-phase recipe
Optimization: CUDA, fp16, DeepSpeed
Quantization: Enabled for efficiency
                
            

                Training Phases (Ettin Recipe)
                
Phase 1: Foundation Training
├── Base model initialization
├── General language understanding
└── Basic security patterns

Phase 2: Specialized Training
├── Prompt injection datasets
├── Attack pattern recognition
└── Fine-tuning for security

Phase 3: Robustness Training
├── Adversarial examples
├── Complex attack scenarios
└── Performance optimization
                
            

Attack Detection Capabilities

Supported Attack Types

DAN Attacks: "Do Anything Now" prompt injections
Role Confusion: Attempts to change AI behavior
System Prompt Leakage: Extraction of internal instructions
Context Manipulation: Long-context prompt injections
Multi-turn Attacks: Conversational prompt injections
Code Injection: Malicious code in prompts

Detection Features

Real-time prompt analysis
Confidence scoring for detected threats
Detailed threat classification
Attack pattern correlation
False positive reduction
Adaptive learning from new threats

Performance Optimization

                Computational Efficiency
                
Optimization Techniques:
├── CUDA Acceleration
│   ├── GPU memory optimization
│   └── Parallel processing
├── Mixed Precision (fp16)
│   ├── Reduced memory usage
│   └── Faster computation
├── DeepSpeed Integration
│   ├── Distributed training
│   └── Model parallelism
└── Quantization
    ├── INT8 inference
    └── Minimal accuracy loss
                
            

Testing and Validation

PINT Testing Framework

Our model is thoroughly tested using the PINT (Prompt Injection Testing) framework to ensure robust performance against various attack vectors.

Test Categories

Basic Injections: Simple prompt manipulation attempts
Advanced Attacks: Complex multi-step injection strategies
Long Context: Extended prompt sequences
Adversarial Examples: Specially crafted attack prompts
Real-world Scenarios: Actual attack patterns from the wild

Dataset Enrichment

Continuous improvement through dataset enrichment with long prompts and complex attack scenarios to maintain high detection accuracy.

Enrichment Strategies

Automated prompt generation
Community-contributed attack patterns
Red team testing results
Real-world incident analysis
Adversarial training examples

Integration and Deployment

API Integration (soon)

ReskLayer provides a simple REST API for easy integration with existing AI systems and applications.

                    API Endpoint Example
                    
POST /api/v1/detect
Content-Type: application/json

{
  "prompt": "User input text here",
  "model_id": "modernbert-difftransformer",
  "threshold": 0.8
}

Response:
{
  "is_injection": true,
  "confidence": 0.95,
  "attack_type": "DAN",
  "risk_level": "high",
  "recommendations": ["Block request", "Log incident"]
}
                    
                

Deployment Options

Cloud Deployment: Fully managed service with automatic scaling
On-Premise: Self-hosted solution for maximum control
Edge Deployment: Lightweight version for edge devices
Hybrid: Combination of cloud and on-premise components

Advanced Features

Pruning and Optimization

Advanced pruning techniques to reduce model size while maintaining detection accuracy.

Optimization Techniques

Structured pruning for attention layers
Knowledge distillation for model compression
Neural architecture search for optimal configurations
Adaptive quantization based on layer importance

Continuous Learning

The model continuously learns from new attack patterns and adapts to emerging threats.

Learning Capabilities

Online learning from new attack patterns
Automatic model retraining
Performance monitoring and alerting
Version control and rollback capabilities

Get Started with ReskLayer

Protect your AI systems from prompt injection attacks with our advanced detection technology.

Request Demo

Technical Support

For technical inquiries and integration support:

contact[@]resk.fr

Contact Our Team