The AI Red Teamer Job Role Path, in collaboration with Google, trains cybersecurity professionals to assess, exploit, and secure AI systems. Covering prompt injection, model privacy attacks, adversarial AI, supply chain risks, and deployment threats, it combines theory with hands-on exercises. Aligned with Google’s Secure AI Framework (SAIF), it ensures relevance to real-world AI security challenges. Learners will gain skills to manipulate model behaviors, develop AI-specific red teaming strategies, and perform offensive security testing against AI-driven applications. The path will be gradually expanded with related modules until its completion.
Hard
110 Sections
110 Sections
Required: 370
Reward: +90
6 Modules included
Medium
24 Sections
Reward: +10
This module provides a comprehensive guide to the theoretical foundations of Artificial Intelligence (AI). It covers various learning paradigms, including supervised, unsupervised, and reinforcement learning, providing a solid understanding of key algorithms and concepts.
24 Sections
Medium
25 Sections
Reward: +10
This module is a practical introduction to building AI models that can be applied to various infosec domains. It covers setting up a controlled AI environment using Miniconda for package management and JupyterLab for interactive experimentation. Students will learn to handle datasets, preprocess and transform data, and implement structured workflows for tasks such as spam classification, network anomaly detection, and malware classification. Throughout the module, learners will explore essential Python libraries like Scikit-learn and PyTorch, understand effective approaches to dataset processing, and become familiar with common evaluation metrics, enabling them to navigate the entire lifecycle of AI model development and experimentation.
25 Sections
Medium
11 Sections
Reward: +10
This module provides a comprehensive introduction to the world of red teaming Artificial Intelligence (AI) and systems utilizing Machine Learning (ML) deployments. It covers an overview of common security vulnerabilities in these systems and the types of attacks that can be launched against their components.
11 Sections
Medium
11 Sections
Reward: +20
This module comprehensively introduces one of the most prominent attacks on large language models (LLMs): Prompt Injection. It introduces prompt injection basics and covers detailed attack vectors based on real-world vulnerability reports. Furthermore, the module touches on academic research in the fields of novel prompt injection techniques and jailbreaks.
11 Sections
Medium
14 Sections
Reward: +20
NEW
In this module, we will explore different LLM output vulnerabilities resulting from improper handling of LLM outputs and insecure LLM applications. We will also touch on LLM abuse attacks, such as hate speech campaigns and misinformation generation, with a particular focus on the detection and mitigation of these attacks.
14 Sections
Hard
25 Sections
Reward: +20
NEW
This module explores the intersection of Data and Artificial Intelligence, exposing how vulnerabilities within AI data pipelines can be exploited, ultimately aiming to degrade performance, achieve specific misclassifications, or execute arbitrary code.
25 Sections