GoKawiil| Read on LessWrong |
Large language models often take actions running on one computer (via an agentic harness such as Claude Code or Codex), however the LLMs’ responses to prompts are computed on a different computer with GPU access. Could a malicious LLM gain control of the host machine where its weights are loaded? Such a machine is a high-value target: it has sufficient compute to run a frontier LLM, offers easy access to the LLM’s weights, and has privileged access to other computers in the datacentre compared with a generic computer on the internet.
This essay explores how easily a malicious LLM could take control of the host machine. The primary attack considered here involves the LLM emitting a token sequence whose semantic meaning is irrelevant but that exploits a vulnerability in the software that loads an LLM onto GPUs, runs the LLM to generate output tokens, and parses those tokens into responses. .
How could an LLM execute code on the host machine?
Like any program, inference engines like vLLM or SGLang may contain exploitable bugs. Because the LLM controls the tokens passed to the inference engine, a malicious LLM could therefore emit a sequence of tokens that a poorly written inference engine mistakes for code or instructions to execute rather than data to return to the user.
But surely all inference engines are robust pieces of software and this would never happen, right?
vLLM previously used eval() on tool-call parameters
CVE-2025-9141 was an arbitrary-code execution bug in vLLM’s XML-based tool parser for Qwen3 Coder. The parser passed almost every tool-call argument to eval() , allowing the LLM to execute arbitrary code on the host machine. Gemini automatically analysed the PR that introduced this bug and correctly flagged it as a critical security vulnerability. Despite that warning, the lead maintainer of vLLM force-merged the PR, writing:
Unfortunately, parsing an arbitrary token sequence into a fully fledged chat (with user turns, assistant responses, tool calls, and so on) is not trivial, and the exact process often differs between LLMs. This complexity creates more opportunities for bugs that could permit arbitrary code execution on the host machine.
vLLM and SGLang are complex, and bugs are common
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