Solutions
One platform. Every AI modality. Zero-Knowledge.
Full AI Spectrum Coverage
The EU AI Act makes no distinction: whether it's a vision model, a chatbot, or an autonomous agent, Article 15 requires robustness testing. RednBlue covers them all.
AI Domains
Test methods
Local
Data extracted
Seven domains, one platform
Computer Vision
Robustness testing for classifiers and object detectors.
- Perturbation attacks
- Detection evasion
- Gradient masking diagnostic
Supported: ResNet, VGG, MobileNet, EfficientNet, ViT, YOLO
Large Language Models
LLM security against prompt attacks and jailbreaks.
- Jailbreak resistance
- System prompt extraction
- Token manipulation (Unicode, homoglyphs)
Compatible: OpenAI, Anthropic, Gemini, Mistral, Cohere, Local LLMs
RAG Pipelines
Security for retrieval-augmented systems.
- Corpus poisoning
- Document-based injection
- Knowledge boundary probing
Detects: LangChain, LlamaIndex, ChromaDB, Pinecone, Weaviate
AI Agents
Security for autonomous agents with tool access.
- Tool hijacking
- Goal drift detection
- Privilege escalation
Frameworks: AutoGen, CrewAI, LangChain Agents
Backdoor Detection
Identifying embedded malicious behaviors.
- Consistency testing under noise
- Trigger enumeration
- Sensitivity probing
IP Protection
Assessment of model theft and cloning risk.
- Extraction feasibility
- Model fingerprinting
- Consistency analysis
Speech Models
Adversarial testing for speech recognition models.
- Imperceptible audio perturbations
- Noise robustness
- Transcription evaluation
Automatic Pipeline Detection
Point RednBlue at your Python file, API endpoint, or LLM provider. Our AST-based detector automatically identifies your architecture and selects the right test suite.
- Framework detection (LangChain, LlamaIndex...)
- System prompt / RAG / tools recognition
- Relevant attack recommendation
Cover your entire AI stack
A single platform to test, certify, and guarantee security for all your AI models.