VLLM

vLLM is an open-source library that optimizes the serving and inference of large language models through high-throughput, low-latency techniques like PagedAttention. For enterprises deploying LLMs in production, vLLM provides critical infrastructure for reducing inference costs and improving response times while maintaining model quality. Organizations using vLLM must still implement proper governance controls around model versions, resource allocation, and output monitoring to ensure compliance and reliability.

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ResearchGlobal2026-08-24

CVE-2025-9141 Makes Inference Engines a Governed Security Dependency

Independent security research published in August 2026 documents a concrete attack class in which a malicious LLM could exploit vulnerabilities in its own inference engine to execute arbitrary code on the host machine. The analysis cites CVE-2025-9141, an arbitrary-code-execution bug in vLLM's tool parser that was merged despite automated critical-severity warnings. Organizations self-hosting open-weight models are advised to separate GPU and token-parsing hosts and treat all inference engine output as untrusted.

vLLM SGLang inference engine security arbitrary code execution self-hosted AI supply chain security CVE-2025-9141 open-weight models