Input tokens are the pieces of text sent into a model before it generates a response. They include user prompts, system instructions, retrieved context, and any copied data the application passes along. In most AI platforms, input tokens directly affect latency and billing: the more text you send, the more processing work the model must do.
In cyber security, input tokens matter because attackers can abuse them. Very long prompts, repeated retries, or prompt stuffing can drive up costs and slow a service, creating a denial-of-wallet risk. Sensitive content included in input tokens can also be exposed in logs or downstream tools if inputs are not filtered. Defenders reduce this risk with strict input limits, truncation, caching, redaction, and rate controls. Monitoring token volume is useful for spotting abnormal usage and for keeping AI systems predictable, secure, and affordable.