CVE-2026-115916

medium

Description

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, vllm/tokenizers/hf.py computes max_token_id with an off-by-one upper bound, allowing a pre-tokenized prompt ID equal to the vocabulary size to pass validation. For a tensor-parallel size of one and an exact multiple-of-64 vocabulary, VocabParallelEmbedding.forward does not mask that value before F.embedding uses it, causing a CUDA device-side assertion and terminating the engine. This issue is fixed in version 0.32.0.

References

https://github.com/vllm-project/vllm/security/advisories/GHSA-q97c-p368-75c4

https://github.com/vllm-project/vllm/releases/tag/v0.32.0

https://github.com/vllm-project/vllm/pull/59491

https://github.com/vllm-project/vllm/commit/37cecc8141e2dcba9dd534897a164d4baaf27229

https://euvd.enisa.europa.eu/vulnerability/EUVD-2026-110036

Details

Source: Mitre, NVD

Published: 2026-10-26

Updated: 2026-10-26

Risk Information

CVSS v2

Base Score: 6.8

Vector: CVSS2#AV:N/AC:L/Au:S/C:N/I:N/A:C

Severity: Medium

CVSS v3

Base Score: 6.5

Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Severity: Medium

EPSS

EPSS: 0.00305