Summary A memory corruption vulnerability that leading to a crash (denial-of-service) and potentially remote code execution (RCE) exists in vLLM versions 0.10.2 and later, in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation.
Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to todense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM.
Details A vulnerability that can lead to RCE from the completions API endpoint exists in vllm, where due to missing checks when loading user-provided tensors, an out-of-bounds write can be triggered. This happens because the default behavior of torch.load(tensor, weightsonly=True) since pytorch 2.8.0 is to not perform validity checks for sparse tensors, and this needs to be enabled explicitly using the torch.sparse.checksparsetensorinvariants context manager.
The vulnerability is in the following code in vllm/entrypoints/renderer.py:148
python def loadandvalidateembed(embed: bytes) -> EngineEmbedsPrompt: tensor = torch.load( io.BytesIO(pybase64.b64decode(embed, validate=True)), weightsonly=True, maplocation=torch.device("cpu"), ) assert isinstance(tensor, torch.Tensor) and tensor.dtype in ( torch.float32, torch.bfloat16, torch.float16, ) tensor = tensor.todense()
Because of the missing checks, loading invalid prompt embedding tensors provided by the user can cause an out-of-bounds write in the call to todense .
Impact All users with access to this API are able to exploit this vulnerability. Unsafe deserialization of untrusted input can be abused to achieve DoS and potentially remote code execution (RCE) in the vLLM server process. This impacts deployments running vLLM as a server or any instance that deserializes untrusted/model-provided payloads.
Fix
https://github.com/vllm-project/vllm/pull/27204
Acknowledgements
Finder: AXION Security Research Team (Omri Fainaro, Bary Levy): discovery and coordinated disclosure.
vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing. Because PyTorch disables sparse tensor invariant checks by default, an attacker can submit crafted embedding requests with malformed (negative or out-of-bounds) tensor indices, when the prompt-embeds feature is enabled, to trigger crashes or resource exhaustion (denial of service), with potential for out-of-bounds/write-what-where memory corruption. This continues CVE-2025-62164, whose prior fix only disabled the feature by default rather than addressing the root cause.
vLLM from 0.10.2 before 0.25.0 contains an uncontrolled resource consumption vulnerability that allows unauthenticated attackers to exhaust memory because the Nemotron-VL processors globally set Image.MAXIMAGEPIXELS to None. Attackers can submit a pathological decompression-bomb image to /v1/chat/completions to OOM-kill the worker and deny service to co-tenants.
vLLM versions >=0.10.2 and <0.28.0 do not apply any audio decode-size or duration limit when extracting audio from video input for NanoNemotronVL models. In nanonemotronvl.py, extractaudiofromvideos calls loadaudiopyav(BytesIO(videobytes)) without the maxdurations or maxdecodebytes parameters, so neither VLLMMAXAUDIODECODEDURATIONS nor VLLMMAXAUDIODECODEBYTES is enforced (unlike the direct audio upload path in AudioMediaIO). When a NanoNemotronVL model is served with useaudioinvideo=True, an attacker who supplies a small, highly compressed video as multimodal input can force the server to allocate gigabytes of memory during audio decoding, resulting in a denial of service. Fixed in vLLM 0.28.0.