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Advisory details: vulnrichment / CVE-2025-62164
Advisory ID vulnrichment/CVE-2025-62164
Aliases
Summary 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.
Status Published
Exploitability 0.5
Weighted Severity 0.0
Risk
Affected and Fixed Packages Package Details
Introduced and Fixed Package Commit Patches Package Commit Patches Details
Weaknesses (4)
System Score Found at
cvssv3.1 8.8
ssvc Track
No exploits are available.

No EPSS data available for this advisory.

Patch URL
There are no known patches.
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H Found at None
Attack Vector (AV) Attack Complexity (AC) Privileges Required (PR) User Interaction (UI) Scope (S) Confidentiality Impact (C) Integrity Impact (I) Availability Impact (A)

network

adjacent_network

local

physical

low

high

none

low

high

none

required

unchanged

changed

high

low

none

high

low

none

high

low

none


Vector: SSVCv2/E:N/A:N/T:T/P:M/B:A/M:M/D:T/2025-11-24T17:15:13Z/ Found at None

Vector: SSVCv2/E:N/A:N/T:T/P:M/B:A/M:M/D:T/2025-11-24T17:15:13Z/

Decision: Track

Source URL: https://github.com/cisagov/vulnrichment/blob/develop/2025/62xxx/CVE-2025-62164.json

Source Advisory: vulnrichment/CVE-2025-62164

View SSVC decision tree
[{'Exploitation': 'none'},
 {'Automatable': 'no'},
 {'Technical Impact': 'total'},
 {'Mission Prevalence': 'minimal'},
 {'Public Well-being Impact': 'material'},
 {'Mission & Well-being': 'medium'}]