AI PAYMENT THREAT · PROMPT-INJECTION PAYMENT CONTAINMENT

Contain prompt injection at the payment boundary

Heuristic injection signals add context, while hard amount, merchant, consent, signature and replay rules remain the enforceable boundary.

Where the failure appears

A page or tool can tell an agent to ignore earlier instructions, reveal secrets or override a budget. Model-level defenses help, but the payment boundary must assume upstream compromise is possible.

Who needs the control

Teams connecting language models to browsers, tools, checkout APIs, wallets or purchasing workflows.

Execution-time response

  1. Treat external page and tool text as untrusted.
  2. Send only normalized payment facts and mandate boundaries.
  3. Treat instruction-override pattern matches as heuristic signals for review, not proof of an attack.
  4. Enforce cryptographic trust and deterministic mandate policy regardless of the model output or heuristic result.

Relevant controls

Supplementary instruction-override signalsDeterministic amount checksMerchant scope enforcementCryptographic purchase bindingOne-time authorization challengesFail-closed trust checks

What changes

MandateShield does not claim complete prompt-injection detection; hard authority controls limit what a manipulated agent can authorize through this boundary.

AI agent builder solution →Security model →Threat intelligence exchange specification →

Reproduce the failure safely

Change a sample amount, seller, consent field or instruction and inspect the structured decision before integrating.

Test the threat free →