A rogue LLM could hijack its own host by attacking the inference engine
The machine holding a model's weights sits right next to the vulnerable code that serves it. Inference servers like vLLM and SGLang are big, buggy C/Python surfaces, and vLLM even shipped eval() on tool-call parameters. Since agentic harnesses execute model output on separate machines, a compromised inference host becomes the real crown jewel, not the model itself. The essay maps the attack chain and what defenses would blunt it.