CVE-2026-34756: vllm · Unthrottled Resource Allocation
Affects vllmSource CVE Database V5
Not known to be exploited · CVSS 6.5 Medium : upgrade to 0.19.0 when convenient.
Data as of 30 Oct 2026, 11:52 UTC
- EPSS
- 0.4% chance of exploitation in 30 days – higher than 36% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 0.19.0
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HExploitation evidence
Disclosed: evidence found.
CVE published 6 Apr 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.
What to do
Upgrade vllm to 0.19.0.
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| vllm-projectvllm | Affected
| Fixed in
|
PyPIvllmpkg:pypi/vllmOther threats | Affected
| Fixed in
|
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
The vulnerability is fixed in vLLM version 0.19.0. Users should upgrade to vLLM 0.19.0 or later to remediate this issue. The Red Hat advisory referencing this CVE does not explicitly confirm a patch or fix availability for their AI Inference Server product, so users should consult the vendor advisory for the latest remediation guidance. Until patched, consider restricting access to the vulnerable API endpoints or implementing request size limits at network or application layers to mitigate exploitation risk.
Description
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.
References
- github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528 (opens in a new tab)
- github.com/vllm-project/vllm/pull/37952 (opens in a new tab)
- github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380 (opens in a new tab)
- access.redhat.com/security/cve/CVE-2026-34756 (opens in a new tab)
- bugzilla.redhat.com/show_bug.cgi?id=2455425 (opens in a new tab)
- security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-34756.json (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:36005 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:36006 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:57380 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:57390 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:57389 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:57387 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:59151 (opens in a new tab)
- access.redhat.com/errata/RHSA-2026:59144 (opens in a new tab)