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.

Attack Vector Network

Attack Complexity Low

Privileges Required Low

Attack Requirements None

User Interaction None

Vulnerable System Confidentiality Impact High

Vulnerable System Integrity Impact High

Vulnerable System Availability Impact High

Subsequent System Confidentiality Impact None

Subsequent System Integrity Impact None

Subsequent System Availability Impact None

Attack Vector Network

Attack Complexity Low

Privileges Required Low

Scope Unchanged

Confidentiality Impact High

Integrity Impact High

Availability Impact High

User Interaction None

No CVSS v3.0

No CVSS v2

This CVE is not in the KEV list.

No EPSS score available.

Key SSVC decision points have not yet been added.

Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).

Vendor Product Default status Versions
vLLM vLLM unaffected
Version Status Constraints
0.10.2 affected < 0.13.0
0.13.0 unaffected —

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Project Subscriptions

Vendors Products
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Advisories

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Fixes

Solution

No solution given by the vendor.

Workaround

No workaround given by the vendor.

References
Link Providers
https://github.com/vllm-project/vllm/security/advisories/GHSA-mcmc-2m55-j8jj cve-icon
https://www.vulncheck.com/advisories/vllm-denial-of-service-via-unvalidated-multimodal-embeddings cve-icon
History

Sat, 20 Jun 2026 18:45:00 +0000

Type Values Removed Values Added
Description 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.
Title vLLM - Denial of Service via Unvalidated Multimodal Embeddings
First Time appeared Vllm
Vllm vllm
Weaknesses CWE-20
CPEs cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*
Vendors & Products Vllm
Vllm vllm
References
Metrics cvssV3_1

{'score': 8.8, 'vector': 'CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H'}

cvssV4_0

{'score': 8.7, 'vector': 'CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N'}

Projects

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cve-icon MITRE

Status: PUBLISHED

Assigner: VulnCheck

Published:

Updated: 2026-06-20T18:27:10.148Z

Reserved: 2026-06-20T13:13:56.012Z

Link: CVE-2026-56340

cve-icon Vulnrichment

No data.

cve-icon NVD

No data.

cve-icon Redhat

No data.

cve-icon OpenCVE Enrichment

No data.

Weaknesses