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.
Weak Model Provenance: Trust Without Proof A critical weakness in today’s AI model landscape is the lack of strong provenance mechanisms. While tools like Model Cards and accompanying documentation attempt to offer insight into a model’s architecture, training data, and intended use cases, they fall short of providing cryptographic or verifiable proof of the model’s …
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.
When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.
While theoretical risks highlight potential harm, real-world scenarios bring the dangers of LLM02:2025 into sharper focus. Below are three attack vectors illustrating how sensitive information disclosure unfolds in practical settings.