Expert AI Red Team Labs

Advanced labs tackling cutting-edge AI security challenges including quantization exploits, reward hacking, agent exploitation, multi-agent attacks, and watermark removal.

Overview

These expert labs go beyond standard prompt injection and jailbreaking. Each lab targets a specific architectural weakness in modern AI systems -- from the numerical instability introduced by quantization to the emergent vulnerabilities in multi-agent orchestration.

Prerequisites

• Completed all intermediate-level labs
• Familiarity with PyTorch or JAX for model manipulation
• Understanding of RLHF training pipelines
• Access to GPU resources (local or cloud) for several labs
• Experience with at least one agent framework (LangChain, AutoGen, or similar)

Lab Index

What Makes These Expert-Level

Unlike beginner and intermediate labs where attack patterns are well-documented, expert labs require you to:

1. Reason about model internals -- understand why an attack works at the architecture level, not just that it works
2. Chain multiple techniques -- combine attack primitives into novel sequences
3. Adapt to defenses -- these targets include state-of-the-art mitigations that you must work around
4. Produce research-quality output -- document findings with the rigor expected in security advisories

Related Topics

• Advanced Labs - Prerequisites that build the skills needed for expert-level exercises
• CTF Challenges - Competitive exercises that combine multiple expert techniques under pressure
• LLM Internals - Technical foundation for understanding architectural vulnerabilities exploited in expert labs
• AI Exploit Development - Research methodology behind expert-level attack development

References

• "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" - Hubinger et al. (2024) - Research on persistent architectural vulnerabilities in safety-trained models
• "Universal and Transferable Adversarial Attacks on Aligned Language Models" - Zou et al. (2023) - Foundational work on architectural attack surfaces in aligned models
• "AI Risk Management Framework" - NIST (2023) - Framework for systematic evaluation of architectural AI risks
• "Scalable Extraction of Training Data from (Production) Language Models" - Nasr et al. (2023) - Expert-level extraction demonstrating deep model internals knowledge