CVE-2026-115916

vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS)

MEDIUM 6.5

At a glance

Source
NVD
EPSS
EPSS 0.30 % · percentile 23 ()
State
PUBLISHED
Published
Updated
Assigner
GitHub_M
NVD status
Awaiting Analysis

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.

Weaknesses (CWE)

  • CWE-129 — Improper Validation of Array Index
  • CWE-193 — Off-by-one Error

References

Scores

VersionScoreVectorScored by
CVSS 3.1MEDIUM 6.5CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HGitHub_M
CVSS 3.1MEDIUM 6.5CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HNVD · security-advisories@github.com (Secondary)

Advisories

European references

Affected products

ProductVersions
vllm-project · vllmaffected < 0.32.0

Sources

SourceLicence
CVE List (CVEProject cvelistV5)CVE Program Terms of Use
EPSS (FIRST.org)FIRST — attribution requested
ENISA EUVDENISA — reproduction authorised, source acknowledged
NVD (NIST) via fkie-cad/nvd-json-data-feedsPublic domain — this product uses data from the NVD API but is not endorsed or certified by the NVD