How Exposed Endpoints Increase Risk Across LLM Infrastructure

"Modern security risks are being introduced less from the models themselves and more from the infrastructure that serves, connects and automates the model. Each new LLM endpoint expands the attack surface, often in ways that are easy to overlook during rapid deployment, especially when endpoints are trusted implicitly. When LLM endpoints accumulate excessive permissions and long-lived credentials are exposed, they can provide far more access than intended."

"Simply put, endpoints allow requests to be sent to an LLM and for responses to be returned. Common examples include inference APIs that handle prompts and generate outputs, model management interfaces used to update models and administrative dashboards that allow teams to monitor performance. Many LLM deployments also rely on plugin or tool execution endpoints, which allow models to interact with external services such as databases that may connect the LLM to other systems."

LLM deployments add internal services and APIs that expand the attack surface beyond the models themselves. Endpoints are interfaces where users, applications, or services send requests to models and receive responses, including inference APIs, model management interfaces, administrative dashboards, and plugin/tool execution endpoints that connect models to external systems. Most endpoints are built for internal use and speed rather than long-term security, leading to poor monitoring and excessive permissions. Long-lived credentials and implicit trust further increase risk. Exposed endpoints have become a common attack vector for cybercriminals to access systems, identities, and secrets powering LLM workloads. Endpoint privilege management must be prioritized.

Read at The Hacker News