Token entropy is a shorthand for token usage that produces little useful output, usually because the same ideas, prompts, or tool calls are repeated. In AI systems, high token entropy means the system is spending more compute and budget than the work justifies.
In cyber security, this matters because AI is increasingly used for triage, detection, and analyst support. Wasteful token patterns can reveal prompt loops, poor context design, or over-automated agents that are making unnecessary decisions. In a defensive stack, monitoring token entropy helps teams spot runaway workflows, reduce cost, and tighten controls around tools and permissions. In attacks, an adversary may try to force repeated model calls, inflate usage, or trigger noisy agent behavior to exhaust resources and distract defenders. Low-value token churn is therefore both an efficiency problem and a security signal.