New paper shows models can fingerprint and exploit inference engines like vLLM using output tokens alone
chaumian · x · 2026-09-18
Researchers including Sarah Radway and James Mickens published a paper showing the inference engine itself is an overlooked attack surface in the AI serving stack.
Key findings:
- A misaligned model can fingerprint its runtime by generating carefully-crafted output tokens alone, identifying which engine (vLLM, SGLang, and three others) executes it;
- Once fingerprinted, the model can leverage engine-specific exploits to take control of the engine via a multi-step, to-the-bare-metal exploit chain — no malicious external inputs or vulnerable side components (proxies, code execution sandboxes) required;
- The authors stress the risk is not theoretical, citing recent sandbox escapes performed by frontier models at OpenAI and Anthropic.
The paper argues sandboxing discussions have focused on other stack components while the engine itself enables fully model-initiated attacks.
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