CVE-2026-93436: Memory exhaustion vulnerability in vLLM inference server
vllm
vLLM fails to properly clean up decode-side metadata for rejected inference requests, allowing remote attackers to exhaust memory. The issue affects deployments up to version 0.29.0.
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
- 8.7
- EPSS
- 0.01 · top 45% · 2026-11-13
- fix status
- Fix status unknown
- first report
- 17 Sep 2026
- last update
- 17 Sep 2026
- CVE ids
Timeline · 1
Sources · 1
Advisories and databases
- 2026-09-17CVE-2026-93436: vLLM through 0.29.0 fails to properly clean up decode-side metadata for rejected inference requests in prefill/decode…NVD · nvd.nist.gov/vuln/detail/CVE-2026-93436
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
- dates
- happened 17 Sep 2026 · first reported 17 Sep 2026
- origin
- NVDVulnerability 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
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the brief as text
# CVE-2026-93436: Memory exhaustion vulnerability in vLLM inference server https://gcdradar.vercel.app/incidents/cve-2026-93436-memory-exhaustion-vulnerability-in-vllm-infer · AIR-2026-0144 · closed 2026-09-17 - Lane: Patch now - Severity: 2 (Disclosed vulnerability) - Fix status: Fix status unknown - First reported: 2026-09-17 - AI role: Attack on AI - Products: vllm - CVE ids: CVE-2026-93436 - CVSS: 8.7 ## Summary vLLM fails to properly clean up decode-side metadata for rejected inference requests, allowing remote attackers to exhaust memory. The issue affects deployments up to version 0.29.0. ## What to do No action recorded; read the sources below and the vendor's advisory. ## Latest updates - 2026-09-17: CVE-2026-93436: vLLM through 0.29.0 fails to properly clean up decode-side metadata for rejected inference requests in prefill/decode… (https://nvd.nist.gov/vuln/detail/CVE-2026-93436) ## Sources - 2026-09-17 NVD: CVE-2026-93436: vLLM through 0.29.0 fails to properly clean up decode-side metadata for rejected inference requests in prefill/decode… https://nvd.nist.gov/vuln/detail/CVE-2026-93436 Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.
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