LLM security

The risk isn't that automated red teaming tools are bad. The risk is that their output looks comprehensive enough to stop asking questions.

Most conversations about LLM security start at the wrong layer. They start with prompts — how to write better system prompts, how to filter inputs, how to add guardrails. But the vulnerabilities that matter most are architectural.

NVIDIA's reference pipeline for safe AI deployment is one of the clearest articulations of what the full lifecycle should look like. The problem is that almost no one follows it completely. The deployment half gets built. The evaluation half gets skipped.

Companies are deploying LLMs into customer-facing systems, internal workflows, and autonomous agents. Most of them haven't accounted for the fact that these models can be manipulated through the very input they're designed to accept.

System prompt extraction isn't one technique — it's a category of attack with at least five distinct patterns. Each exploits a different aspect of how models process instructions. Here's how they work and how to test your own deployment against each one.

Organizations are treating AI security as something entirely new — a problem that requires new teams, new frameworks, and new thinking from scratch. It doesn't. The principles are the same. The attack surface is different.

A system prompt is the most misunderstood component in an LLM deployment. Teams spend weeks choosing models, tuning parameters, and building integrations — then write the system prompt in an afternoon.