CVE-2026-115916
vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS)
CVSS Score
6.5
EPSS Score
0.0%
EPSS Percentile
0th
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.
| CWE | CWE-129 CWE-193 |
| Vendor | vllm-project |
| Product | vllm |
| Published | Oct 26, 2026 |
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CVSS v3 Breakdown
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High
Affected Versions
vllm-project / vllm
< 0.32.0
References
github.com: https://github.com/vllm-project/vllm/security/advisories/GHSA-q97c-p368-75c4 github.com: https://github.com/vllm-project/vllm/pull/59491 github.com: https://github.com/vllm-project/vllm/commit/37cecc8141e2dcba9dd534897a164d4baaf27229 github.com: https://github.com/vllm-project/vllm/releases/tag/v0.32.0