Anti-detect browsers pose a significant challenge for fraud prevention teams as they allow fraudsters to manipulate device fingerprints and impersonate new users, making traditional detection methods ineffective. Originally obscure, these browsers have become mainstream tools for fraudsters to conduct large-scale attacks like credential stuffing and bonus abuse by bypassing device intelligence checks. They work by spoofing browser attributes, disrupting fingerprinting scripts, and supporting browser automation to disguise repetitive fraudulent activities. Traditional detection methods fall short as they rely on surface-level signals like user agents and IP addresses, which anti-detect browsers can easily manipulate. However, advanced detection systems like Fingerprint offer a solution by using over 100 device, browser, and network signals to create unique visitor IDs, combined with Smart Signals that detect inconsistencies indicative of anti-detect browser usage. These systems layer behavioral, network, and device intelligence to build comprehensive risk profiles, enabling fraud teams to stay ahead of increasingly sophisticated fraud attempts while maintaining a smooth experience for legitimate users.