AI assistants are being bolted onto products faster than anyone is threat-modeling them. The result is a new, very exploitable attack surface where the model itself becomes a way around your business rules.

TL;DR

An LLM-powered chatbot will happily do what a cleverly worded request tells it to — including revealing data or granting outcomes it was never meant to. If the model (not your server) is the thing enforcing pricing, access, or anonymity, an attacker can talk their way past it. Keep authorization and business rules on the server, and treat the model's output as untrusted.

The new attack surface

Traditional business-logic bugs live in forms and APIs. With an AI front-end, the same class of bug moves into natural language: manipulate a parameter the model trusts, or phrase a request just so, and it can be steered into disclosing hidden identifiers, applying discounts, or de-anonymizing records.

Why scanners miss it

Automated scanners look for known technical flaws in requests and responses. They do not reason about whether a conversation coaxed the assistant into breaking a business rule — that takes a human who understands both the app's logic and how LLMs can be manipulated.

The fix

• Never let the model be the access-control or pricing boundary. Enforce every rule server-side, after the model responds.
• Authorize the action, not the phrasing of the request.
• Log and rate-limit assistant-driven actions like any other sensitive endpoint.

This is core to our API penetration testing and AI/LLM red teaming. Faseel Suite helps you test these surfaces continuously.