A KV cache is the stored “key” and “value” attention data a transformer model keeps during inference so it does not have to recompute earlier tokens on every new step. By reusing this state, the model can answer faster and with less GPU work, especially during long conversations or long-context requests.
In cyber security, KV cache behavior matters because it affects cost, latency, and availability of AI services. Attackers can try to exhaust memory by sending very long prompts, forcing cache growth, or causing repeated cache misses that trigger expensive recomputation. Defenders watch cache hit rate, VRAM headroom, and token throughput to spot abuse and protect service quality. Efficient KV cache handling is also a core defense for large-model hosting, where inference bottlenecks can become denial-of-service targets.