gcdRadar
archive/Patch now/AIR-2026-0238
closedclosed 5 Oct 2026Patch now2 Disclosed vulnerabilityAI

vLLM GLMGA video sampling permits CPU and memory exhaustion

vLLM

Maliciously structured video sampling options allow callers to consume excessive CPU and memory resources in vLLM. This impacts concurrent processing and delays unrelated requests.

disclosed CVENo confirmed exploitation, so this record is tracked as a disclosed vulnerability and not counted as an incident. It moves into the counts if CISA or the vendor confirms exploitation. Why

CVSS
5.3
EPSS
<0.01 · top 78% · 2026-11-13
EPSS scores by FIRST
fix status
Fix status unknown
first report
5 Oct 2026
last update
5 Oct 2026
CVE ids
CVE-2026-105760other records

Timeline · 2

  1. 5 Oct 2026CVE-2026-105760: vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion
  2. 5 Oct 2026CVE-2026-105760: vLLM is an inference and serving engine for large language models.

Sources · 2

Advisories and databases

  1. 2026-10-05
    CVE-2026-105760: vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion
    GitHub Advisory Database · github.com/advisories/GHSA-58v5-2m8f-94pr
  2. 2026-10-05
    CVE-2026-105760: vLLM is an inference and serving engine for large language models.
    NVD · nvd.nist.gov/vuln/detail/CVE-2026-105760

Titles and links belong to their publishers. The summary above is written by gcdRadar in its own words; data is CC BY-SA 4.0.

Classification

lane
Patch now
AI role
Attack on AI
categories
Infrastructure vulnerability
severity
2 · Disclosed vulnerability
fix status
Fix status unknown
targets
Framework / tooling
owasp llm top 10
LLM03:2025 Supply Chain
mitre atlas
no technique for these categories
tags
resource-exhaustionvllm
dates
happened 5 Oct 2026 · first reported 5 Oct 2026
origin
GitHub Advisory Database
Vulnerability record from NVD / the GitHub Advisory Database. This product uses the NVD API but is not endorsed or certified by the NVD.
record
confidence 0.95 · created 2026-10-18 06:33Z · updated 2026-10-18 06:33Z

Lane, AI role, categories and severity come from the classifier; the OWASP and ATLAS rows are derived from the categories. How these are assigned

Copy a brief

This record as a short Markdown brief, for a ticket, a chat or a coding agent.

the brief as text
# vLLM GLMGA video sampling permits CPU and memory exhaustion

https://gcdradar.vercel.app/incidents/vllm-glmga-video-sampling-permits-cpu-and-memory-exhaustion · AIR-2026-0238 · closed 2026-10-05

- Lane: Patch now
- Severity: 2 (Disclosed vulnerability)
- Fix status: Fix status unknown
- First reported: 2026-10-05
- AI role: Attack on AI
- Products: vLLM
- CVE ids: CVE-2026-105760
- CVSS: 5.3

## Summary

Maliciously structured video sampling options allow callers to consume excessive CPU and memory resources in vLLM. This impacts concurrent processing and delays unrelated requests.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2026-10-05: CVE-2026-105760: vLLM is an inference and serving engine for large language models. (https://nvd.nist.gov/vuln/detail/CVE-2026-105760)
- 2026-10-05: CVE-2026-105760: vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion (https://github.com/advisories/GHSA-58v5-2m8f-94pr)

## Sources

- 2026-10-05 GitHub Advisory Database: CVE-2026-105760: vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion https://github.com/advisories/GHSA-58v5-2m8f-94pr
- 2026-10-05 NVD: CVE-2026-105760: vLLM is an inference and serving engine for large language models. https://nvd.nist.gov/vuln/detail/CVE-2026-105760

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.

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