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Response snapshot Oct 29, 2026, 01:26 UTC

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VulnerabilityCVE-2026-115916

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

For a model whose vocabulary size is an exact multiple of the embedding padding size (64) — e.g.

At a glance

Exploited
Not known
Severity
6.5 Medium (CVSS 3.1)
Chance of exploitation
Not estimated
Sources
1 source

What to do

GitHub lists first patched versions: vllm: 0.32.0. Confirm the affected range and upgrade guidance in the exact advisory.

Read GitHub-reviewed advisory GHSA-q97c-p368-75c4 (opens a new tab)

What happened

For a model whose vocabulary size is an exact multiple of the embedding padding size (64) — e.g. any Llama-2-family model, vocab 32000 — a single request carrying a pre-tokenized prompt with token id equal to the vocab size (32000) passes vLLM's input-id validation but is one past the last embedding row. At tensor-parallel size 1 the embedding lookup does not mask indices, so the id reaches F.

embedding out of range and raises a CUDA device-side assert, which poisons the engine's CUDA context and takes the engine down. Any client that can send pre-tokenized input (/v1/completions with prompt as token ids, /tokenize-fed flows, or the kv_transfer_params["prompt_token_ids"] reuse path) can trigger it.

Root cause

vllm/v1/engine/input_processor.py accepts a token id when id 1 masks via get_masked_input_and_mask`, so the id is clamped, not an OOB — likely wrong output rather than a crash. - Models whose vocab is a multiple of 64 with tokenizer.vocab_size one past the max real id (Llama-2 family, vocab 32000, is the clear case). Qwen2.5 is NOT affected (padded headroom).

Continue reading

Suggested fix

max_token_id in vllm/tokenizers/hf.py now uses tokenizer.vocab_size - 1, so an id equal to the vocabulary size fails input validation instead of reaching the embedding. Fixed in [MR59491](https://github.com/vllm-project/vllm/pull/59491), released in v0.32.0.

Reporter

Shijie Lyu (GitHub shijie-lyu lshjhf@gmail.com). Reported privately per SECURITY.

This advisory was fixed in [MR59491](https://github.com/vllm-project/vllm/pull/59491).

Affected package ranges reported by GitHub: vllm: < 0.32.0

Scores and how they were measured

CVSS base severity

6.5

Version 3.1As of

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

Measurement source (opens a new tab)

How bad it is if exploited. Not a prediction of exploitation or proof you are exposed.

EPSS probability

Not available

A model's estimate of exploitation in the next 30 days, not certainty or your own risk.

Related technology

Sources

  1. Primary source

    GitHub-reviewed advisory GHSA-q97c-p368-75c4 (opens a new tab)

    The public advisory supplies the description, affected package/version ranges, and any listed CVSS metric. GitHub review is not local review.

    Published Checked

  2. Primary source

    GitHub public advisory API page (opens a new tab)

    The API page supplied GHSA-q97c-p368-75c4, its actual identifiers, source dates, and structured metrics.

    Published Date not suppliedChecked

Cite this record

For factual claims, cite the primary sources above; cite KEVWire for its compilation, dates, and classifications.

APA

KEVWire. (2026, October 28). vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS) (CVE-2026-115916). https://kevwire.com/record/CVE-2026-115916

MLA

"vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS) (CVE-2026-115916)." KEVWire, 28 Oct. 2026, kevwire.com/record/CVE-2026-115916.

Chicago

KEVWire. "vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS) (CVE-2026-115916)." Last modified October 28, 2026. https://kevwire.com/record/CVE-2026-115916.

Evidence summary (plain text)

vLLM: Out-of-vocabulary prompt token id passes input validation and triggers a CUDA device-side assert (engine crash / DoS) (Oct 28, 2026).
For a model whose vocabulary size is an exact multiple of the embedding padding size (64) — e.g. any Llama-2-family model, vocab 32000 — a single request carrying a pre-tokenized prompt with token id equal to the vocab size (32000) passes vLLM's input-id validation but is one past the last embedding row. At tensor-parallel size 1 the embedding lookup does not mask indices, so the id reaches F.embedding out of range and raises a CUDA device-side assert, which poisons the engine's CUDA context and takes the engine down.
CVSS 6.5 (version 3.1; evidence as of Oct 28, 2026; https://github.com/advisories/GHSA-q97c-p368-75c4).
Sources:
GitHub-reviewed advisory GHSA-q97c-p368-75c4: https://github.com/advisories/GHSA-q97c-p368-75c4
GitHub public advisory API page: https://api.github.com/advisories?type=reviewed&per_page=100&sort=updated&direction=desc
Record ID: CVE-2026-115916.
Record origin: Ingested record.
AI evidence: Not assessed.
Known exploitation: Not assessed.
Zero-day status: Not assessed. KEV membership alone does not establish zero-day status.
About this data

This record was ingested from a data source. Retrieved: Oct 29, 2026, 00:55 UTC. Ingestion is not an independent editorial investigation.

A CISA KEV listing documents known exploitation, not necessarily a zero-day; absence from the catalog does not prove a vulnerability has never been exploited.

Technology tags indicate possible relevance, not verified exposure. Confirm the affected product, version, and configuration against your own inventory; KEVWire does not scan your systems.

A personal reading checklist on this device, not verified remediation.

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