Explore AI & LLM Security

Here's the part most security courses skip entirely: an LLM reads instructions and data through the exact same channel. There's no separate wire for "trusted commands" versus "content the model happened to look at." That single fact is why a summarization agent browsing a poisoned web page can be compromised before a human ever types a word.

You'll work through the OWASP LLM Top 10 the way an attacker actually thinks about it — not as a checklist to memorize, but as a map of where an agent's tools, permissions, and reading list turn into attack surface. Nugget starts you where the real damage lives: not "can I make the model say something bad" but "what can this agent DO, and with whose credentials, once it reads something it shouldn't trust?"

Along the way you'll test the instinct that a well-worded system prompt is a fence. It isn't — it's a suggestion sharing a channel with whatever the attacker just fed the model. Once that lands, the rest of the domain — RAG poisoning, tool abuse, context exfiltration — stops looking like a grab-bag of exploits and starts looking like one recurring shape.