Detecting forged browser fingerprints for bot detection, lessons from LinkedIn

This article examines LinkedIn’s client-side bot detection fingerprinting, highlighting getHasLiedOs and getHasLiedLanguages, which flag inconsistencies between claimed OS/language and signals like userAgent, oscpu, platform, touch capabilities, and language preferences. It shows how cross-attribute consistency checks reliably expose spoofed fingerprints common in bot traffic and traces the logic back to the open-source FingerprintJS project. The piece underscores that diverse, stable signals and consistency validation remain foundational to effective fingerprinting-based defenses.