AI in the real world: scams with fake images and what devs can do

Fraud doesn't wait for regulation. There are reports of scams using AI to generate fake evidence (images) and exploit refund and support flows. For devs, that becomes a product requirement: trust and audit.

How fraud takes advantage

Refunds with fake "proof", support with generated images, creation of profiles or documents. Any flow where "money leaves" or "a decision is made" based on evidence becomes a target.

Warning signs in the product

Many refunds in a short time. Repeated pattern of photos or angles. Inconsistent metadata (EXIF, timestamps, device).

Technical defenses

Rate limit on sensitive requests. Sampling audit (human + AI). Immutable logs of decisions. Risk score per user. The lesson: any "money leaves the system" flow must be treated as an attack surface.

Key takeaways

Treat refund and support as attack surface. Rate limit, audit and immutable logs help.

Read also

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• 120+ agent tools: how to choose without falling for the hype

FAQ

AI-generated image detection? Tools exist but evolve; use as a signal, not the only defense. Combine with behavior patterns and human audit.

What about privacy? Logs and scores should be minimized and retained only as needed; document in the privacy policy.