Fraud Prevention in the Age of AI Agents

A major challenge for almost every online service is reliably identifying users, and knowing whether a given user or agent can be trusted. Historically, web services leaned on cookies stored in the browser to remember a visitor and recognize them when they return. The problem is that cookies live in the user’s own browser and are easy to remove, block, or avoid entirely, so a bad actor can simply clear them to escape recognition. IP blocking and CAPTCHA puzzles are firmer approaches, but they can frustrate legitimate users while doing little to stop a determined attacker. That leaves developers stuck between being too lax and losing control, or too strict and driving real users away.

FingerprintJS was started in 2012 as an open-source project. In 2019, Fingerprint was launched as a separate, full commercial platform, built on the original FingerprintJS GitHub library, to recognize devices and browsers without relying on cookies. It handles identification by reading over 100 signals, from fonts and screen resolution to how a device renders audio and graphics.

Keshia Rose works in Developer Relations at Fingerprint. In this episode she joins Gregor Vand to talk about fingerprinting, how Fingerprint combines multiple signals to identify fraudulent actors, how to distinguish legitimate AI agent use from anonymous or malicious bots, and how Fingerprint’s research team uncovered and disclosed a privacy vulnerability in Firefox and Tor that could have been used to track people across the web.

Full Disclosure: This episode is sponsored by Fingerprint

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