LLM token theft: how attackers drain your AI startup's bottom line

Running an AI product with a free tier often results in token theft and abuse, where attackers create multiple fake accounts to exploit free services without any intention of paying. This abuse involves tactics like mass account creation, use of stolen credit cards, and account reselling, with the stolen access being used for various purposes such as automated content generation and running coding tasks. The traffic generated by such abuse is characterized by automated signups with randomized usernames and abnormal token use, which can lead to significant operational disruptions. Traditional security tools often miss these abuses due to their focus on network-layer signals rather than application-specific behaviors. Effective mitigation requires making abuse economically irrational through strategies like device fingerprinting, email domain reputation checks, velocity scoring, and SMS challenges, as well as leveraging cross-product intelligence to adapt to evolving threats. As attackers continue to adapt quickly, relying on dedicated services like Radar can help manage this ongoing threat without diverting resources from core product development.

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