Microsoft's disclosure of LLM-mediated redirects to cryptojacking sites marks a qualitative shift from SEO poisoning to model-level manipulation. Attackers are no longer limited to gaming search rankings; they now exploit the trust users place in generative responses, surfacing malicious links for GPU-intensive tools like CrystalDiskInfo and FurMark. This approach deliberately selects high-value endpoints rather than casting a wide net, maximizing mining yield per infection while enabling secondary access via ScreenConnect for potential ransomware or data exfiltration. What mainstream reporting overlooks is the convergence with prior LLM supply-chain experiments: the same gleeze.com infrastructure and Dynu-hosted domains echo patterns seen in 2025 campaigns against developer forums, where poisoned package recommendations bypassed traditional vetting. The technique extends beyond simple link injection, suggesting adversaries may be fine-tuning prompts or scraping model outputs to refine targeting of high-performance hardware. Connections to broader LLM weaponization appear in parallel research from Unit 42 on prompt-injection vectors and CrowdStrike's observations of AI-generated phishing lures scaling credential theft. These incidents signal that large language models are transitioning from auxiliary tools to primary distribution channels, lowering the barrier for financially motivated actors to achieve persistent access on valuable systems. Defenders must now treat chatbot outputs as untrusted surfaces equivalent to search results, implementing behavioral monitoring for sideloaded DLLs and anomalous ScreenConnect deployments.