Prompt injection becomes a financial security problem when manipulated AI output can influence a privileged transaction path.
An AI assistant may understand:
"Refund this transaction."
It may retrieve the correct payment and even generate a valid tool call.
But that tool call should remain a proposal — not authorization.
Keep authorization outside the LLM
Sensitive financial actions should still pass through deterministic backend controls that validate:
- Authenticated identity
- Transaction ownership
- Current transaction state
- Limits and policy rules
- Step-up authentication or approval
- Idempotency and duplicate execution protection
The model can interpret intent and prepare an action, but it should not decide whether money is actually allowed to move.
Indirect Prompt Injection
The malicious instruction may also come from:
- Uploaded documents
- Emails
- Support tickets
- Merchant content
- Retrieved knowledge
Even if that content successfully influences the model, the surrounding platform should still reject unauthorized financial actions.
Smaller Tools, Smaller Blast Radius
Prefer narrow tools such as:
get_transaction()
prepare_refund()
request_refund()
instead of giving the model broad internal API or execution access.
The Architectural Principle
Prompt injection detection is useful, but it should not be the only security boundary.
Assume the model can be manipulated. Design the financial path so model compromise does not automatically become authorization compromise.
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