AI-Red-Teaming-Playground-Labs

AI Red Teaming Playground Labs This repository contains the challenges for the labs used in the course "AI Red Teaming in Practice". The course was originally taught at Black Hat USA 2024 by Dr. Amanda Minnich and Gary Lopez. Martin Pouliot handled the infrastructure and scoring for the challenge...

ai-llm-red-team-handbook

AI / LLM Red Team Field Manual & Consultant's Handbook A comprehensive operational toolkit for conducting AI/LLM red team assessments on Large Language Models, AI agents, RAG pipelines, and AI-enabled applications. This repository provides both tactical field guidance and strategic consulting...

SecML

SecML: Una librería para Machine Learning Seguro y Explicable SecML es una librería de código abierto en Python para la evaluación de seguridad de algoritmos de Machine Learning ML. Viene con un conjunto de potentes características: Amplia gama de algoritmos ML soportados. Todos los algoritmos de...

Zero-Trust Federated Learning for Connected Aftermarket Devices

Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust...

SECUREVENT: Hybrid AI/ML Security Monitoring for Distributed Event-Based Systems

Distributed event-based systems have become a common substrate for Internet-scale publish/subscribe services, IoT telemetry, cloud-native microservices, and security operations pipelines. Their loose coupling and asynchronous delivery improve scalability, but they also expand the attack surface:...

New whitepaper outlines the taxonomy of failure modes in AI agents

We are releasing a taxonomy of failure modes in AI agents to help security professionals and machine learning engineers think through how AI systems can fail and design them with safety and security in mind. The taxonomy continues Microsoft AI Red Team's work to lead the creation of systematizati...

Cyberattacks against machine learning systems are more common than you think

Machine learning ML is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with...

Cyberattacks against machine learning systems are more common than you think

Machine learning ML is making incredible transformations in critical areas such as finance, healthcare, and defense, impacting nearly every aspect of our lives. Many businesses, eager to capitalize on advancements in ML, have not scrutinized the security of their ML systems. Today, along with...