The #1 security mistake I see in AI systems

After a stack of security assessments on AI products, one gap shows up more than any other: no real input validation between the user and the model. Teams treat the LLM as a trusted black box, pipe user text straight into it, and hand it tool access on top. An attacker turns that setup into an incident with prompt injection.

The danger

Give a model tools, database queries, API calls, the ability to send email, and untrusted input that reaches the prompt can steer them. The model reads your instructions and instructions hidden in user content the same way, unless you separate the two. A crafted message, or text pulled from a document or a web page, redirects what the model does.

Closing the gap

You close most of the risk by layering controls.

1. Validate and constrain input before it reaches the model. Enforce length limits, reject unexpected formats, flag known injection patterns.
2. Separate instructions from data. Keep the system prompt apart from user content and mark the boundary so the model can tell them apart.
3. Prefer structured outputs. A schema shrinks the blast radius of a manipulated response.
4. Gate the tools on the server. Authorize every tool call yourself. A model suggesting an action should never trigger it.
5. Rate limit and monitor for anomalous patterns and attempts to leak the system prompt.

The takeaway

Prompt injection has no clean fix yet, but most real damage comes from systems that skipped the steps above. Treat model input the way you treat any user input, as untrusted, and you close the majority of the risk.

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