AI and data stack advisories

Severe, 6 weeks459Projects324

459 severe, 6 weeks · 324 projects

vLLMGHSA-q97c-p368-75c4

vLLM: improper array index validation

vLLM

CVE-2026-115916 · Published Oct 28, 2026

Medium6.5
Fix: upgrade to 0.32.0 or later
GitHub advisory

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 <= max(tokenizer.max_token_id, model_config.get_vocab_size() - 1).

vllm/tokenizers/hf.py computes max_token_id = max(max(vocab.values()), tokenizer.vocab_size). For Llama-2 tokenizers max(vocab.values()) = 31999 but tokenizer.vocab_size = 32000, so max_token_id = 32000 , one greater than the largest valid id.

The engine bound therefore becomes max(32000, 31999) = 32000, and id 32000 is accepted.

The model's embedding has pad_vocab_size(32000, 64) = 32000 rows (no padding), valid indices 0..31999. At VocabParallelEmbedding.forward with tp_size == 1 there is no masking, so...

Affected versions

PackageAffectedFixed in
vllm
PyPI
< 0.32.00.32.0
Details and references

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 <= max(tokenizer.max_token_id, model_config.get_vocab_size() - 1)`. `vllm/tokenizers/hf.py` computes `max_token_id = max(max(vocab.values()), tokenizer.vocab_size)`. For Llama-2 tokenizers `max(vocab.values()) = 31999` but `tokenizer.vocab_size = 32000`, so `max_token_id = 32000` , one greater than the largest valid id. The engine bound therefore becomes `max(32000, 31999) = 32000`, and id 32000 is accepted. The model's embedding has `pad_vocab_size(32000, 64) = 32000` rows (no padding), valid indices 0..31999. At `VocabParallelEmbedding.forward` with `tp_size == 1` there is no masking, so index 32000 hits `F.embedding` out of range. ## Affected - tp=1 (single GPU per model , common per-copy layout). tp>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). ## 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).

CVSS 3.1
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Severity from
GitHub (reviewed advisory)
Weakness
CWE-129, CWE-193
Also known as
CVE-2026-115916

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