# adversarial-ml
標記為「adversarial-ml」的 8 篇文章
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.
Foundations
Essential building blocks for AI red teaming, covering red team methodology, the AI landscape, how LLMs work, embeddings and vector systems, AI system architecture, and adversarial machine learning concepts.
Lab: Adversarial ML From Scratch
Hands-on expert lab for implementing gradient-based adversarial attacks against language models from scratch without frameworks, building intuition for how adversarial perturbations exploit model gradients.
Counterfit Walkthrough
Complete walkthrough of Microsoft's Counterfit adversarial ML testing framework: installation, target configuration, running attacks against ML models, interpreting results, and automating adversarial robustness assessments.
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。
基礎
AI 紅隊演練的核心建構區塊，涵蓋紅隊方法論、AI 景觀、大型語言模型如何運作、嵌入向量與向量系統、AI 系統架構，以及對抗性機器學習概念。
實驗室: Adversarial ML From Scratch
Hands-on expert lab for implementing gradient-based adversarial attacks against language models from scratch without frameworks, building intuition for how adversarial perturbations exploit model gradients.
Counterfit 導覽
Complete walkthrough of Microsoft's Counterfit adversarial ML testing framework: installation, target configuration, running attacks against ML models, interpreting results, and automating adversarial robustness assessments.