Protecting against inference theft

In the context of AI endpoint security, inference theft poses a significant risk due to the high costs associated with AI calls, compared to the relatively inexpensive HTTP requests. This type of theft involves unauthorized use and resale of AI inference, which can lead to substantial financial losses for AI operators. Traditional defenses like IP rate limits and authentication walls are insufficient because attackers can easily bypass these measures using residential proxies and disposable accounts. Sophisticated attackers can adapt custom AI endpoints to be compatible with standard platforms, enabling them to resell stolen inference at a fraction of the cost. A real incident at Vercel demonstrated the effectiveness of using Vercel's BotID for deep analysis to protect against such attacks by verifying every AI request individually, rather than at the session level. This approach helps prevent attackers from amortizing their bypass costs across multiple calls, leveraging the cost asymmetry between expensive inference and cheap verification. Implementing such verification strategies can mitigate the risk of inference theft and protect the financial and operational integrity of AI services.