Securing AI Systems

LLM applications break the assumption every other security control is built on: that instructions and data are separable. In an LLM, data is instructions. Every design that reads untrusted content and then acts has to be evaluated with that in mind, and no amount of prompt engineering fixes it.

When to Use

• Security review of an LLM-backed feature, chatbot, or copilot
• Threat modeling an agentic system: tools, autonomy, memory, multi-agent
• Reviewing an MCP server, tool definition, or plugin surface
• Assessing a RAG pipeline for data leakage and poisoning
• Evaluating model, dataset, and dependency supply chain
• Red teaming an AI system with authorization

When NOT to Use