Protecting against token theft

Inference theft is a significant challenge for AI systems, where unauthorized users exploit AI inference endpoints to conduct costly operations without incurring expenses, subsequently reselling the stolen resources at a lower price. This type of theft can dramatically increase operational costs for AI service providers, as seen in cases where traffic spikes lead to tens of thousands of dollars in potential losses. Traditional defenses such as IP rate limits and authentication walls often prove inadequate due to attackers using sophisticated measures like residential proxies and custom adapters to bypass security. To combat this, it is crucial to implement verification on every AI request rather than just at the session level. Vercel employs BotID deep analysis, a tool that effectively distinguishes between legitimate users and bots without visible challenges, thereby protecting endpoints by evaluating each request individually. This approach helps maintain normal traffic levels and prevent significant financial damage from inference theft, demonstrating the importance of robust AI endpoint security in mitigating the risk of unauthorized access and resource exploitation.