HiddenLayer AI Detection and Response

HiddenLayer AI Detection and Response for GenAI fortifies your machine learning deployments against unauthorized access, infiltration attempts, and intellectual property theft - all while delivering real-time protection. Streamline and scale the safeguarding of AI models for seamless real-time security, intercepting traffic to and from GenAI applications, empowering users with the ability to proactively block harmful transactions or promptly generate alerts. AIDR defends against evolving threats, including inference attacks, evasion, and model theft. Achieve scalability and unobtrusive protection without causing disruptions to your workflow.


Key Benefits:

Automated: Leverage automated processes to detect and respond to AI model breach attempts, providing a proactive defense mechanism.
Scalable: Get clear reporting on detected threats, empowering security teams with insights into adversarial behavior. Identify and report on various adversarial activities such as model theft, reconnaissance, evasion, misclassification, and other potential threats.
Unobtrusive: Detections are made via our platform without requiring any access to models and the data that powers them.

Key Capabilities:

- Prompt Injection - Ensure inputs to your LLM do not cause unintended consequences.
- Data Leakage - Ensure LLM outputs do not expose backend systems risking privilege escalation or remove code execution.
- MITRE ATLAS Integration - MLDR maps to 64+ Adversarial AI attack tactics & techniques
- Protects against real-time model Inference Attacks
- Protects against Model Tampering - Know where the model is weak and when the model has been tampered with
- Protects against Prompt Injection/Model Injection - Protect the model from it's inputs or outputs being deliberately changed
- Protects against Model Extraction/Theft - Stop reconnaissance attempts through inference attacks which could result in your model intellectual property being stolen
- Uses a combination of Supervised Learning, Unsupervised Learning, Dynamic/Behavioral Analysis and Static Analysis to deliver detection for a library of adversarial machine learning attacks