Upstream information
Description
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.SUSE information
Overall state of this security issue: Does not affect SUSE products
This issue is currently rated as having important severity.
| CVSS detail | CNA (GitHub) |
|---|---|
| Base Score | 8.8 |
| Vector | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H |
| Attack Vector | Network |
| Attack Complexity | Low |
| Privileges Required | Low |
| User Interaction | None |
| Scope | Unchanged |
| Confidentiality Impact | High |
| Integrity Impact | High |
| Availability Impact | High |
| CVSSv3 Version | 3.1 |
SUSE Timeline for this CVE
CVE page created: Fri Aug 14 17:27:26 2026CVE page last modified: Sun Aug 16 13:26:40 2026