{
 "id": "AIR-2025-0374",
 "slug": "cve-2025-62372-vllm-vulnerable-to-denial-of-service-via-mult",
 "title": "CVE-2025-62372: vLLM vulnerable to denial of service via multimodal embedding inputs",
 "summary": "Users could crash the vLLM inference engine serving multimodal models by supplying embedding inputs with incorrect shapes. The issue allowed unauthenticated denial of service attacks against the engine.",
 "type": "attack_on_ai",
 "categories": [
  "infra_vuln"
 ],
 "severity": 2,
 "targets": [
  "framework_or_tooling"
 ],
 "products": [
  "vLLM"
 ],
 "incident_date": "2025-11-20",
 "incident_date_precision": "day",
 "first_reported": "2025-11-20",
 "sources": [
  {
   "url": "https://github.com/advisories/GHSA-pmqf-x6x8-p7qw",
   "publisher": "GitHub Advisory Database",
   "title": "CVE-2025-62372: vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs",
   "published_at": "2025-11-20"
  },
  {
   "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-62372",
   "publisher": "NVD",
   "title": "CVE-2025-62372: vLLM is an inference and serving engine for large language models (LLMs).",
   "published_at": "2025-11-21"
  }
 ],
 "source_count": 2,
 "confidence": 0.95,
 "status": "published",
 "origin": "seed_ghsa",
 "tags": [
  "CVE-2025-62372",
  "vllm",
  "dos"
 ],
 "created_at": "2026-10-18T07:00:14.101Z",
 "updated_at": "2026-10-18T10:04:21.885Z"
}