I just read a comprehensive guide on LLM security and red teaming that covers everything from foundational vulnerabilities to real-world exploits. Basically, how ChatGPT can give you the recipe for cooking meth and security lapses in AI.
Book Name: AI Safety and redteaming book Mohammad Arsalan
What's Inside:
7 Chapters covering:
LLM internals (transformers, attention, tokenization)
Complete AI risk landscape (training to deployment)
Prompt injection and jailbreaking mechanics
Advanced evasion tactics (encoding, obfuscation, multimodal attacks)
Real production vulnerabilities (n8n RCE, GitHub Copilot exploits, Claude Computer Use C2)
Defensive strategies and guardrails (NeMo, DeepEval, Llama Guard)
Practical red teaming playbooks with code
Why This Matters:
With enterprises deploying agentic AI (MCP, A2A frameworks, tool-enabled systems), the attack surface is exploding. This book bridges the gap between AI engineering and security practice.
Real exploits covered:
Cross-agent privilege escalation
Data exfiltration via DNS/images
Unicode smuggling and ASCII injection
Multimodal prompt injection
Supply chain poisoning
Who It's For:
AI safety researchers, security engineers, ML practitioners, red teamers, anyone building or defending LLM systems
Includes code implementations, demos, and links to 50+ GitHub repos and research papers.
Written by AI safety researchers with hands-on experience in LLM security evaluation and adversarial AI.
by swap_019