MIT ML Device
Purpose: Facilitates communication and data exchange between machine learning devices and systems. Enables the deployment and execution of machine learning models for various applications.
Vulnerabilities: Model Poisoning: Injection of malicious data to manipulate machine learning outcomes. Adversarial Attacks: Crafting inputs to deceive machine learning models and produce incorrect outputs. Model Inversion: Reverse-engineering machine learning models to extract sensitive training data. Privacy Violations: Disclosure of sensitive information through model outputs or training data. Model Theft: Unauthorized access to and theft of proprietary machine learning models.
Tools: CleverHans, IBM Adversarial Robustness Toolbox (ART), OpenAI Gym
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