| Gói / sản phẩm | Phiên bản bị ảnh hưởng | Phiên bản đã vá |
|---|---|---|
| vllm | ≥ 0.11.1, < 0.32.0 | 0.32.0 |
| vllm | ≥ 0.11.1, < 0.32.0 | 0.32.0 |
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:
# 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:
# 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.
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Quét site miễn phíVerified 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.
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_IMAGE_PIXELS cap of 178,956,970 (vllm/envs.py:86).
2. The zero guard turns a valid image into a None encoder output. _parse_and_validate_image_input (deepseek_ocr.py:468-497) returns None on the exact-zero sum; embed_multimodal (:606-611) propagates that None to its caller. The same pattern exists in deepseek_ocr2.py:339 (if pixel_values is None or torch.sum(pixel_values).item() == 0: return None), and UnlimitedOCRForCausalLM (unlimited_ocr.py:244) inherits the method unmodified. (A sibling check in _encode_local_features at deepseek_ocr.py:522-523 guards crop tensors, which pass through a lossy resize and are not needed for this report.)
3. The worker asserts on the None and the engine dies. With the default configuration (cudagraph_mm_encoder=False, config/compilation.py), the V2 model runner executes the encoder eagerly: execute_model (v1/worker/gpu/model_runner.py:1849) → model_states/default.py:108 prepare_inputs_embeds → encoder_runner.execute_mm_encoder (v1/worker/gpu/mm/encoder_runner.py:168) → model.embed_multimodal(**mm_kwargs_batch) → None → sanity_check_mm_encoder_outputs(batch_outputs, expected_num_items=1) (encoder_runner.py:170) → AssertionError at v1/worker/utils.py:512. There is no per-request try/except anywhere on this path; the exception reaches the busy loop's top-level except Exception at core.py:1368 (via step_with_batch_queue at core.py:660 and collective_rpc), which logs "EngineCore encountered a fatal error.", sends engine-dead, and re-raises (fault tolerance off by default; engine_core_sentinel re-raises). The API server's output_handler receives EngineDeadError (async_llm.py:843) and shuts down. In the verified run the container exited (code 0, OOMKilled=false) and the port stayed closed — no self-recovery.
4. Why the guard exists and why it is wrong here. The torch.sum(...) == 0 → return None shortcut comes from the upstream DeepSeek-OCR reference implementation, where it skipped empty batches during batch inference. In vLLM, embed_multimodal is only invoked when the scheduler has real multimodal items to encode, so the guard never protects anything — it is purely a landmine that converts one specific, perfectly legal pixel distribution into a contract violation (embed_multimodal must return embeddings, not None) that the engine treats as a fatal invariant breach.
Version history. The check was introduced together with the model itself in #27247 ("[Model] Upstream Deepseek-OCR model", commit 675aa2ec, 2025-10-22; first stable release v0.11.1) and is unchanged since. DeepSeek-OCR-2 support (#33165, 2026-02-02) carries the same check at deepseek_ocr2.py:339. On main (checked 2026-09-30) the guard in _parse_and_validate_image_input is still present verbatim.
Any unauthenticated client that can reach the OpenAI-compatible API of a deployment serving DeepSeek-OCR, DeepSeek-OCR-2, or Unlimited-OCR can deterministically crash the entire serving instance with one request: the EngineCore process dies, every in-flight and future request fails, and the API server exits; absent a supervisor the deployment stays down until restarted. The trigger is a schema-valid chat request with a small, completely legal PNG — for an OCR service, a half-black/half-white bitmap is indistinguishable from ordinary scanned-document traffic. The attack costs one request (~0.1 s observed end-to-end) and can be repeated after every restart.
No special server flags, no malformed input, no resource exhaustion, and no race are involved; the only requirement is that the trigger image is alone in its per-step encoder batch (true on an idle instance and retryable otherwise). Default-configured deployments of these models are affected with no mitigation short of patching.
Suggested remediation (any one of these breaks the kill chain):
torch.sum(pixel_values) == 0 → return None shortcut from _parse_and_validate_image_input (and the _encode_local_features sibling) in deepseek_ocr.py / deepseek_ocr2.py: in vLLM an empty encoder batch never reaches embed_multimodal, so the guard protects nothing and only converts valid inputs into a contract violation. If an all-zeros fast-path is ever needed, return a correctly-shaped zero embedding, not None.embed_multimodal return contract inside the worker.sanity_check_mm_encoder_outputs failures should fail the offending request rather than the EngineCore busy loop — a model returning None for one item should not be engine-fatal.This advisory was fixed in MR59417.