What breaks in AI systems under adversarial pressure. Documented from the Intrenex Lab.
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.
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.
Most organizations that have started tracking AI risk are using their existing risk register format with a few AI-specific line items added. That's a start — but the entries that matter most are the ones that don't map cleanly to traditional IT risk categories.
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.
Controlled adversarial testing against real AI systems. Methodology documented. Findings published.
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