{
 "id": "AIR-2026-0238",
 "slug": "vllm-glmga-video-sampling-permits-cpu-and-memory-exhaustion",
 "title": "vLLM GLMGA video sampling permits CPU and memory exhaustion",
 "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.",
 "type": "attack_on_ai",
 "categories": [
  "infra_vuln"
 ],
 "severity": 2,
 "targets": [
  "framework_or_tooling"
 ],
 "products": [
  "vLLM"
 ],
 "incident_date": "2026-10-05",
 "incident_date_precision": "day",
 "first_reported": "2026-10-05",
 "sources": [
  {
   "url": "https://github.com/advisories/GHSA-58v5-2m8f-94pr",
   "publisher": "GitHub Advisory Database",
   "title": "CVE-2026-105760: vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion",
   "published_at": "2026-10-05"
  },
  {
   "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-105760",
   "publisher": "NVD",
   "title": "CVE-2026-105760: vLLM is an inference and serving engine for large language models.",
   "published_at": "2026-10-05"
  }
 ],
 "source_count": 2,
 "confidence": 0.95,
 "status": "published",
 "origin": "seed_ghsa",
 "tags": [
  "CVE-2026-105760",
  "resource-exhaustion",
  "vllm"
 ],
 "created_at": "2026-10-18T06:33:31.967Z",
 "updated_at": "2026-10-18T06:33:31.967Z"
}