AI 驅動紅隊演練
Advanced1 min readUpdated 2026-03-15
使用 AI 自動化與擴展 AI 安全測試——涵蓋 PAIR、TAP、LLM 攻擊者框架與強化學習攻擊最佳化。
AI 驅動紅隊演練將 AI 反向對付 AI——使用語言模型產生、最佳化並擴展對抗性攻擊。這從根本上改變安全測試的經濟學。
Learning Path
0/5 completed~50 min total5 lessons
- 112mPAIR & TAP 攻擊 Algorithmsexpert
Implementation and analysis of PAIR (Prompt Automatic Iterative Refinement) and TAP (Tree of 攻擊s with Pruning) algorithms for automated jailbreak generation.
- 29mLLM-as-攻擊er Optimizationexpert
Techniques for optimizing LLMs as adversarial attack generators: prompt engineering for attack models, context management, diversity optimization, and attacker model selection.
- 39mMulti-代理 攻擊 Coordinationexpert
Coordinated multi-agent attack strategies against AI systems: role-based agent architectures, conversation orchestration, collaborative jailbreaking, and swarm-based adversarial testing.
- 410mRL-Based 攻擊 Optimizationexpert
Using reinforcement learning to train adversarial attack policies against AI systems: reward design, policy architectures, curriculum learning, and transferability of learned attacks.
- 510mScalable Oversight Challengesadvanced
How oversight breaks down as AI systems become more capable: the scalable oversight problem, recursive reward modeling, debate, market-making, and implications for red teaming increasingly capable models.