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bdu:2025-14675

certfr-2025-avi-1138 — De multiples vulnérabilités ont été découvertes dans VMware Tanzu Platform. Elles permettent à un attaquant de provoque…

certfr-2025-avi-1138

EUVD-2026-261778

fkie_cve-2025-62164

vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists 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 to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.

GHSA-mrw7-hf4f-83pf — vLLM deserialization vulnerability leading to DoS and potential RCE

Affected: PyPI: vllm

### 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 to_dense(). 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, weights_only=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.check_sparse_tensor_invariants](https://docs.pytorch.org/docs/stable/generated/torch.sparse.check_sparse_tensor_invariants.html) context manager.

The vulnerability is in the following code in [vllm/entrypoints/renderer.py:148](https://github.com/vllm-project/vllm/blob/a332b84578cdc0706e040f6a765954c8a289904f/vllm/entrypoints/renderer.py#L148)

```python def _loa…

PYSEC-2026-2018 — vLLM deserialization vulnerability leading to DoS and potential RCE

Affected: PyPI: vllm

The vulnerability is in the following code in [vllm/entrypoints/renderer.py:148](https://github.com/vllm-project/vllm/blob/a332b84578cdc0706e040f6a765954c8a289904f/vllm/entrypoints/renderer.py#L148)

```python def _loa…

RHSA-2025:23204 — Red Hat Security Advisory: Red Hat AI Inference Server 3.2.5 (CUDA)

openssl: Out-of-bounds read & write in RFC 3211 KEK Unwrap libxslt: libxml2: Inifinite recursion at exsltDynMapFunction function in libexslt/dynamic.c golang.org/x/oauth2/jws: Unexpected memory consumption during token parsing in golang.org/x/oauth2/jws golang.org/x/crypto/ssh: Denial of Service in the Key Exchange of golang.org/x/crypto/ssh runc: container escape with malicious config due to /dev/console mount and related races firefox: thunderbird: expat: libexpat in Expat allows attackers to trigger large dynamic memory allocations via a small document that is submitted for parsing vllm: VLLM deserialization vulnerability leading to DoS and potential RCE vllm: vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs ray: Ray is vulnerable to RCE via Safari & Firefox Browsers through DNS Rebinding Attack vllm: vLLM: Remote Code Execution via malicious model configuration github.com/sigstore/fulcio: Fulcio: Denial of Service via crafted OpenID Connect (OIDC) token

WID-SEC-W-2025-2666 — vllm und PyTorch: Schwachstelle ermöglicht DoS und potenzielle Codeausführung

Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm und PyTorch ausnutzen, um einen Denial-of-Service-Zustand zu verursachen oder möglicherweise eine Remote-Codeausführung zu erreichen.