When AI infrastructure becomes the target: Securing gateways and control points
Microsoft Threat Intelligence has documented active attacks against exposed AI infrastructure, including exploitation of LiteLLM gateways, credential theft, persistence techniques, and cryptomining. The findings show that AI deployment components are becoming distinct targets for attackers, not just the models themselves.
Why this matters: AI security conversations focus almost entirely on what models say or generate. The more immediate problem is the infrastructure holding everything together. Gateways, API proxies, and control points like LiteLLM sit between your applications and the models they call. If an attacker gets into that layer, they can harvest credentials, hijack compute, and stay hidden while your AI keeps running normally. Companies rushing to deploy AI often secure the model and forget the plumbing. That is where the real access lives.
Who should care: Cybersecurity · Privacy officers · Administrators · General readers · AI governance · Policy
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