At its core, Unbounded Consumption refers to an LLM application’s failure to impose constraints on inference usage—resulting in an open door for resource abuse. Unlike traditional software vulnerabilities that might involve code injection or data leakage, Unbounded Consumption exploits the operational behaviour of the model itself—by coercing it into performing an excessive number of inferences.
As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.
Prompt injection is not just a vulnerability — it’s a multi-headed threat vector. From overt attacks to inadvertent leakage, each scenario introduces unique risks, requiring tailored strategies to safeguard operational integrity, regulatory compliance, and business reputation.
Infrastructure as Code is a modern DevOps practice that codifies and manages IT infrastructure through version-controlled files. It enables consistent, repeatable, and scalable deployment of infrastructure resources.
Explainable AI refers to methods and techniques that make the decision-making processes of AI systems comprehensible to humans. Unlike traditional software with deterministic logic, most AI models learn patterns from data, making their internal workings difficult to understand.