AS The Hacker News explains, organisations running their own LLMs are broadening the attack surface by deploying more internal services and APIs to support those models, with risks arising less from the models themselves and more from the surrounding infrastructure. Each new LLM endpoint can become a security boundary, especially when endpoints accumulate excessive permissions or long‑lived credentials are exposed, enabling far more access than intended.
The article outlines common exposure patterns such as publicly accessible APIs without authentication, weak or static tokens, assumptions that internal means safe, temporary test endpoints that become permanent, and cloud misconfigurations that expose services to the internet. It stresses that exposed endpoints allow attackers to move laterally across trusted environments, potentially enabling prompt‑driven data exfiltration, abuse of tool‑calling permissions and indirect prompt injection.
It also highlights Non‑Human Identities (NHIs) as a particular risk in LLM environments, where service accounts and API keys can grant broad permissions that, if exposed, are difficult to rotate or revoke, according to Keeper. To reduce risk, the piece advocates applying zero‑trust principles, enforcing least‑privilege access, Just‑in‑Time access, monitoring privileged sessions, rotating secrets, and removing long‑lived credentials, emphasising endpoint privilege management as central to securing AI infrastructure.