Unbounded consumption in large language models (LLMs) is a security vulnerability that enables attackers to make excessive and uncontrolled inference requests, leading to denial-of-service attacks, economic losses, model theft, and service degradation. Sophisticated threat actors exploit the unique computational characteristics of transformer architectures and pay-per-use cloud pricing models to target high-value models, such as Claude, generating over $46,000 in daily consumption costs. To detect unbounded consumption attacks, teams should start with token velocity tracking, expand into comprehensive resource monitoring, and deploy machine learning for attack pattern recognition. Defense-in-depth strategies include building smart input validation, implementing adaptive resource controls, deploying security-first monitoring architecture, and structuring incident response for speed and learning. Implementing a specialized platform like Galileo provides integrated monitoring capabilities to detect sophisticated consumption attacks before they cause significant damage.