# fundamentals
標記為「fundamentals」的 19 篇文章
Fundamentals Practice Exam
25-question practice exam covering LLM fundamentals, prompt injection basics, safety mechanisms, red team methodology, and AI threat landscape at an intermediate level.
Practice Exam 1: AI Red Team Fundamentals
25-question practice exam covering LLM architecture, prompt injection, agent exploitation, defense mechanisms, and red team methodology at an intermediate level.
Fundamentals Study Guide
Study guide covering LLM architecture basics, security terminology, threat models, attack categories, and the OWASP LLM Top 10 for assessment preparation.
Adversarial ML: Core Concepts
History and fundamentals of adversarial machine learning — perturbation attacks, evasion vs poisoning, robustness — bridging classical adversarial ML to LLM-specific attacks.
How LLMs Work: A Red Teamer's Guide
Understand the fundamentals of large language models — token prediction, context windows, roles, and temperature — through a security-focused lens.
Red Team Methodology Fundamentals
What AI red teaming is, how it differs from traditional security testing, and the complete engagement lifecycle from scoping to reporting.
Red Teaming Fundamentals for AI
Fundamental concepts and methodology for AI red teaming including goal setting, scope definition, technique selection, and reporting.
Lab: Embedding Fundamentals for Red Teamers
Learn embedding fundamentals including vector similarity, semantic search, and how embeddings enable RAG systems.
Lab: Introduction to Safety Testing
Learn the fundamentals of LLM safety testing including test case design, baseline measurement, and result documentation.
Prompt Injection & Jailbreaks
A comprehensive introduction to prompt injection — the most fundamental vulnerability class in LLM applications — and its relationship to jailbreak techniques.
基礎 Practice Exam
25-question practice exam covering LLM fundamentals, prompt injection basics, safety mechanisms, red team methodology, and AI threat landscape at an intermediate level.
Practice Exam 1: AI 紅隊 基礎
25-question practice exam covering LLM architecture, prompt injection, agent exploitation, defense mechanisms, and red team methodology at an intermediate level.
章節評量：防禦基礎
15 題校準評量，測試你對 AI 防禦機制基礎的理解。
學習指南：基礎
AI 紅隊基礎認證考試的學習指南——涵蓋核心概念、關鍵術語、學習資源與練習問題。
對抗式 ML：核心概念
對抗式機器學習的歷史與基本原理——擾動攻擊、逃避與投毒、穩健性——將古典對抗式 ML 銜接至 LLM 特有攻擊。
紅隊演練 基礎 for AI
Fundamental concepts and methodology for AI red teaming including goal setting, scope definition, technique selection, and reporting.
實驗室: Embedding 基礎 for 紅隊ers
Learn embedding fundamentals including vector similarity, semantic search, and how embeddings enable RAG systems.
實驗室: 介紹 to Safety Testing
Learn the fundamentals of LLM safety testing including test case design, baseline measurement, and result documentation.
提示詞注入與越獄
提示詞注入的完整入門——大型語言模型應用程式中最根本的漏洞類別——以及它與越獄技術的關係。