Medium severity · published Oct 5, 2026
CVE-2026-105760
vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion
5.3
CVSS
0.3%
EPSS
No
On KEV
Description
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Confidentiality
None
Integrity
None
Availability
Low
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.3% · higher than 22% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
VL
vllm · vllm-project
>= 0.23.0rc2, < 0.30.0 · source: vendor, NVD
References
NVD
https://nvd.nist.gov/vuln/detail/CVE-2026-105760
CVE.org
https://www.cve.org/CVERecord?id=CVE-2026-105760
x_refsource_CONFIRM
https://github.com/vllm-project/vllm/security/advisories/GHSA-58v5-2m8f-94pr
x_refsource_MISC
https://github.com/vllm-project/vllm/pull/54935
x_refsource_MISC
https://github.com/vllm-project/vllm/commit/8b6de0eb9a09ef53f20cf06bd4d17ee264b9c2a7
x_refsource_MISC
https://github.com/vllm-project/vllm/releases/tag/v0.30.0
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