AI Red Teaming Topics

Essential building blocks for AI red teaming — methodology, AI landscape, LLM fundamentals, and threat modeling.

Transformer architecture, tokenizer exploitation, alignment bypass, embedding attacks.

Advanced injection techniques, automated jailbreak research, multimodal attack vectors.

AI agent attacks, multi-agent/A2A protocol exploitation, MCP tool surface attacks.

RAG pipeline poisoning, training data attacks, model extraction and IP theft.

Model serialization RCE, AI infra exploitation, application security patterns.

Cloud AI security for red teamers — shared responsibility models, attack surfaces, and cloud-native AI exploitation.

LLM fingerprinting, system prompt extraction, AI-specific threat modeling.

Custom adversarial tools, red team C2 frameworks, continuous automated red teaming.

End-to-end red team engagement methodology, reporting, and remediation.

Understanding AI defenses from the attacker perspective — defense categories and bypass strategies.

Advanced LLM internals — deep dives into model architecture and exploit primitives.

Injection research and automation — systematic approaches to discovering novel injection vectors.

Advanced agentic exploitation — complex multi-agent attack chains and tool-use abuse.

Data and training attacks — advanced techniques for poisoning, extraction, and manipulation.

Advanced tradecraft — operational techniques for AI red team engagements.

Defensive strategies for AI systems — guardrails architecture, monitoring, observability, and content filtering.

Training pipeline security — pre-training attacks, fine-tuning exploitation, alignment bypass, and RLHF manipulation.

Multimodal AI security — attacking image, audio, video, and cross-modal processing in AI systems.

Hands-on lab exercises from beginner to expert — CTF challenges and practical AI red teaming drills.

Cutting-edge AI security research — reasoning model attacks, computational exploits, and emerging threat vectors.

References and cheat sheets — quick-reference materials for AI red teamers.

AI governance frameworks, legal and ethical considerations, evaluation benchmarking, and compliance requirements.

Professional skills for AI red teamers — operations management, report writing, and client engagement.

Real-world AI security incidents, domain-specific case studies, and platform-level security analysis.

Real-world consequences of AI attacks — misinformation, harmful content generation, and systemic risk assessment.

Industry-specific AI security challenges — healthcare, finance, legal, and critical infrastructure red teaming.

Forensic investigation and incident response for AI systems — evidence collection, attribution, and containment.

Security across the LLMOps lifecycle — data preparation, experiment tracking, deployment, and monitoring.

AI coding assistant vulnerabilities — suggestion poisoning, training data attacks, and code generation exploitation.

Model-specific security analysis — architecture-level vulnerabilities across GPT, Claude, Llama, and more.

Fine-tuning as an attack vector — safety alignment removal, data poisoning through fine-tuning APIs.

Embedding attack surfaces — vector database security, embedding injection, and similarity search exploitation.

Step-by-step guided walkthroughs — red team tools, engagement methodology, and defense implementation guides.

Assessment suite for validating AI red teaming knowledge — section assessments, practice exams, and skill verification.

Monthly AI red teaming challenges — earn points, share results, and grow your skills with the community.