CVE-2026-115914: vLLM DeepSeek-OCR image processing vulnerable to denial of service

## Summary Any vLLM deployment serving a model of the DeepSeek-OCR family — `deepseek-ai/DeepSeek-OCR`, `deepseek-ai/DeepSeek-OCR-2`, or `baidu/Unlimited-OCR` (whose `UnlimitedOCRForCausalLM` subclasses `DeepseekOCRForCausalLM`) — can be **killed by a single unauthenticated chat request carrying an ordinary, schema-valid PNG image**, under completely default server configuration. The image model's input validation contains a guard inherited from the upstream DeepSeek-OCR reference implementation: ```python # vllm/model_executor/models/deepseek_ocr.py:478-479 (_parse_and_validate_image_input) if torch.sum(pixel_values).item() == 0: return None ``` In the original research code this was an empty-batch shortcut; in vLLM's serving architecture `pixel_values` is the actual client image tensor, and the condition is **exactly satisfiable by a legitimate image**: the in-tree `DeepseekOCRProcessor` normalizes pixels with `ToTensor + Normalize(0.5, 0.5)` (`vllm/transformers_utils/processors/deepseek_ocr.py:120-140`), mapping pixel value 0 → exactly −1.0 and 255 → exactly +1.0 in float32. A 1024×1024 image that is half pure black and half pure white (a perfectly ordinary 1-bit-style scanned-document PNG for an OCR model) therefore yields `torch.sum(pixel_values) == 0.0` **exactly** (equal counts of ±1.0; pairwise reduction stays integer-exact, well inside float32's 2²⁴ exactness bound). `ImageOps.pad(image, (1024, 1024))` is the identity map for an already-1024×1024 image (`processors/deepseek_ocr.py:350-355`), so no padding pixels disturb the balance. Returning `None` makes `embed_multimodal` return `None` (`deepseek_ocr.py:606-611`), which immediately trips the engine-side sanity check in the worker: ```python # vllm/v1/worker/utils.py:512 (sanity_check_mm_encoder_outputs) assert isinstance(mm_embeddings, (list, tuple, torch.Tensor)), ( "Expected multimodal embeddings to be a list/tuple of 2D tensors, ...") ``` The `AssertionError` is raised inside the encoder runner of the EngineCore worker process with no per-request containment, escapes to the busy loop's top-level handler (`vllm/v1/engine/core.py:1368`, "EngineCore encountered a fatal error."), triggers `EngineDeadError` at the API server (`vllm/v1/engine/async_llm.py:843`), and the whole process tree shuts down permanently. `/health` refuses connections until a manual restart. Verified end-to-end on the official `vllm/vllm-openai:v0.30.0` image (id d076d77d8dad) with real `deepseek-ai/DeepSeek-OCR` weights: after confirming `/health` 200, a text chat 200, and two control images (all-white and all-black 1024×1024) 200, the single half-black/half-white request returned HTTP 500 (`EngineCore encountered an issue`), the engine log shows `AssertionError: Expected multimodal embeddings to be a list/tuple of 2D tensors ... got <class 'NoneType'>` at `v1/worker/utils.py:512`, the API server shut itself down, and the container exited (`Exited (0)`, `OOMKilled=false`). Every subsequent request, including `GET /health`, fails at the socket. The vulnerable check is unchanged on `main` (verified 2026-09-30 against the raw file on the default branch). **Environment notes (disclosed for completeness; unrelated to the sink):** (1) the v0.30.0 image's Triton version refuses to compile `deepencoder.py`'s fused rel-pos attention kernel (`LOG2E` non-constexpr global) during the startup profile run; per the compiler's own suggestion we set `TRITON_ALLOW_NON_CONSTEXPR_GLOBALS=1` — an environment workaround only, no source file was modified. (2) DeepSeek-OCR ships no `chat_template`, so verification passed a one-line `--chat-template` that concatenates message content text; this does not touch the image data path. (3) The zero-sum check aggregates over the whole per-step modality batch, so the trigger image must be alone in its encoder batch — trivially true on an idle instance (our verification: first attempt, single request), and simply retried otherwise. ## Details **1. The processor maps an ordinary binary image to exactly ±1.0 pixels.** `DeepseekOCRProcessor.tokenize_with_images` (`vllm/transformers_utils/processors/deepseek_ocr.py:338-389`) builds the global view as `ImageOps.pad(image, (base_size, base_size), color=mean*255)` with `base_size = 1024` (`:21`). For a 1024×1024 input the pad is a no-op (same-size `ImageOps.contain` resampling is the identity: the bicubic kernel passes through sample points exactly). `ImageTransform` (`:120-140`) then applies `T.ToTensor()` (uint8 0 → 0.0, 255 → 1.0, exact in float32) and `T.Normalize((0.5,)*3, (0.5,)*3)` (0.0 → −1.0, 1.0 → +1.0, exact). A left-half-black/right-half-white 1024×1024 RGB image therefore produces 1,572,864 elements of −1.0 and 1,572,864 of +1.0; `torch.sum` (pairwise/tree reduction, partial sums bounded by 1.58M ≪ 2²⁴) evaluates to exactly `0.0`, and `.item() == 0` is `True`. All of this runs in the API process and passes every validation layer: the image is 1 MP, far under the default `VLLM_MAX_I

• github.com/vllm-project/vllm/security/advisories/GHSA-gq5q-89c7-vhvx (opens in a new tab)
• nvd.nist.gov/vuln/detail/CVE-2026-115914 (opens in a new tab)
• github.com/vllm-project/vllm/pull/59417 (opens in a new tab)
• github.com/vllm-project/vllm/commit/c15672cfb49152ebf65cfd188e71b84593ec5686 (opens in a new tab)
• github.com/vllm-project/vllm (opens in a new tab)
• github.com/vllm-project/vllm/releases/tag/v0.32.0 (opens in a new tab)

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