AI & LLM Systems Security Guide

Analyze the fundamental architectural flaw in Transformer LLMs: the lack of physical separation between control instructions and untrusted data.

Dissect real-world prompt injection payloads, indirect data exfiltration vectors, and multimodal evasion techniques with real CVE case studies.

Master retrieval-augmented generation security: document poisoning, cross-tenant semantic bleed, embedding inversion, and vector database authorization flaws.

Explore the 2025-2026 threat frontier: excessive agency, tool-use hijacking, malicious Model Context Protocol servers, and shadow agent loops.

Inspect the downstream impact of trusting raw LLM output: XSS in frontend clients, Server-Side Request Forgery via browsing tools, and sandbox escapes in code interpreters.

Analyze how LLMs memorize pre-training data, Carlini's repeated token divergence attacks, and system prompt exfiltration mechanics.

Dissect weaponized machine learning model checkpoints: arbitrary code execution via Python pickle in PyTorch, Safetensors security, and Hugging Face malware.

Architect resilient multi-layered AI defenses: Llama Guard, NeMo Guardrails, Dual-LLM privilege separation, and automated red teaming with Garak and Promptfoo.