Quick answerRun a dedicated financial-exploitation red team exercise before launching any AI agent capability that can grant a discount, refund, price match, credit, or fee waiver, and again any time that capability's rules change, not only when the security team's separate prompt-injection testing cycle happens to come around. The two exercises test different failure modes: security red-teaming asks whether someone can break the agent's guardrails; financial-exploitation red-teaming asks whether someone can talk a perfectly well-behaved agent into an outcome it was technically allowed to offer, using persistence, escalation threats, or plausible-sounding hardship stories, none of which require breaking anything.
Why this needs its own testing team and its own scenarios
A prompt-injection red team is typically staffed and scripted to look for ways to override system instructions or extract hidden prompts. That team is not naturally equipped to test whether the agent folds under a customer who calmly repeats a refund request five different ways, threatens to cancel, or claims a hardship the policy has a legitimate exception for. Staff this exercise with people who understand the discount and refund policy in detail, ideally including someone from the team that actually approves human-agent exceptions today, since they already know the manipulation patterns that work on people and can adapt them for testing an AI agent. This is not the same test as an ongoing prompt injection testing program, which should continue in parallel, not be replaced by this.
What good financial-exploitation scenarios look like
Effective scenarios mimic real gaming patterns rather than obvious abuse: a customer who claims a competitor is offering a lower price and asks for a match the agent is not authorized to give without proof, a customer who escalates a small billing question into a request for a much larger goodwill credit, and a customer who tests whether repeating the same request in a new conversation eventually reaches an agent instance willing to approve it. Run each scenario multiple times across different session states, since an agent that holds firm once but caves on the third attempt in a new conversation has a real gap. Feed the results into the same incentive-design review covered in are your incentives quietly training teams to game the AI agent's metrics, since a containment-rate or resolution-time KPI can quietly reward an agent configuration that approves more concessions.
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Make this a recurring exercise, not a one-time launch gate
Refund and discount policies change, promotions come and go, and an agent tuned to resist gaming under one policy set can develop new gaps under another. Treat a policy or pricing change with the same weight as a model or prompt change for red-teaming purposes, and re-run the exercise before the new rules go live, not after a pattern of unauthorized concessions shows up in a monthly cost review. Pair this with the broader autonomous-payment testing already covered in testing an AI agent before letting it issue refunds or process payments autonomously, which focuses on the mechanics of the payment action itself; this exercise focuses on whether a human customer can talk the agent into taking that action in the first place.
FAQ
Is this different from testing for prompt injection that unlocks a discount code? Yes. Prompt injection is a technical bypass of the agent's instructions. Financial-exploitation testing assumes the agent is working exactly as designed and asks whether its designed behavior can still be talked into an unintended concession through ordinary conversation.
Should the red team include people posing as legitimate hardship cases? Yes, deliberately blur that line. The hardest cases to design policy around are the ones that are genuinely sympathetic but still outside policy, and those are exactly the cases most likely to produce an inconsistent agent response worth catching before launch.
How do you measure success for this kind of exercise? Track the rate at which each scenario type succeeds in extracting an unauthorized concession, and treat any non-zero rate on a scenario the policy clearly prohibits as a finding that needs a fix before launch, not a rounding error.