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Fencer Vulns
medium 6.5

CVE-2026-34755 json

vLLM Affected by Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing

published 2026-04-03 7 mo ago modified 2026-09-10 1 mo ago also EUVD-2026-19350 , GHSA-pq5c-rjhq-qp7p , PYSEC-2026-144

cvss
6.5
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.5%
EPSS 0.5% chance of exploitation in 30 days, percentile 41%
public exploits
0
none known

Summary

vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM. This vulnerability is fixed in 0.19.0.

Patch / fix

  • pypi vllm 0.19.0
  • Red Hat Red Hat AI Inference Server 3.2 1782951012, 1782951244
  • Red Hat Red Hat AI Inference Server 3.4 1787151769, 1787151771, 1787151774, 1787151840
  • Red Hat Red Hat Enterprise Linux AI 3.4 1787243961, 1787244006, 1787253774, 1787253912, 1787253989, 1787254059, 1787310717

Vendor advisories

What the vendors themselves say about their products: affected, fixed or not affected, and what to do.

red hat product security vex CVE-2026-34755 updated cvss 6.5

vLLM: vLLM: Denial of Service due to excessive video frame processing

  • Red Hat AI Inference Server rhaiis/vllm-tpu-rhel9, rhaiis/vllm-neuron-rhel9 Red Hat OpenShift AI (RHOAI) rhoai/odh-vllm-cpu-rhel9, rhoai/odh-vllm-cuda-rhel9, rhoai/odh-vllm-rocm-rhel9, rhoai/odh-vllm-gaudi-rhel9, rhoai/odh-kserve-agent-rhel9, rhoai/odh-kserve-router-rhel9, +2 more
    affected: workaround, no fix available “Fix deferred”
  • Red Hat AI Inference Server 3.2 rhaiis/vllm-cuda-rhel9 1782951012, rhaiis/vllm-rocm-rhel9 1782951244
    fixed: vendor fix RHSA-2026:36005
  • Red Hat AI Inference Server 3.4 rhaii/vllm-cpu-rhel9 1787151769, rhaii/vllm-cuda-rhel9 1787151771, rhaii/vllm-rocm-rhel9 1787151774, rhaii/vllm-spyre-rhel9 1787151840
    fixed: vendor fix RHSA-2026:57380
  • Red Hat Enterprise Linux AI 3.4 rhelai3/bootc-cuda-rhel9 1787243961, rhelai3/bootc-rocm-rhel9 1787244006, rhelai3/bootc-aws-cuda-rhel9 1787253912, rhelai3/bootc-gcp-cuda-rhel9 1787253774, rhelai3/disk-image-cuda-rhel9 1787310717, rhelai3/bootc-azure-cuda-rhel9 1787253989, +1 more
    fixed: vendor fix RHSA-2026:59151

Red Hat CSAF VEX © Red Hat, Inc., from https://security.access.redhat.com/data/csaf/v2/vex/; changed: reduced to product statuses and remediations. CC BY 4.0 · csaf

CISA SSVC

Exploitation
none
Automatable
no
Technical impact
partial
Assessed
7 mo ago

Exploit prediction

0.48% chance of exploitation in the next 30 days, higher than 41% of all scored CVEs (FIRST EPSS, 2026-11-08).

CVSS scores

assigned by version score vector
CNA 3.1 6.5 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CISA ADP 3.1 6.5 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
GitHub 3.1 6.5 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
OSV 3.1 6.5 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Affected packages

ecosystem package affected fixed in
pypi vllm
>=0.7.0, <0.19.0
0.19.0

Affected products

  • vllm-project vllm
  • Red Hat Red Hat AI Inference Server 3.2
  • Red Hat Red Hat AI Inference Server 3.4
  • Red Hat Red Hat Enterprise Linux AI 3.4
  • Red Hat Red Hat AI Inference Server
  • Red Hat Red Hat OpenShift AI (RHOAI)

Classification

Weakness
  • CWE-770 Allocation of Resources Without Limits or Throttling

References

Sources

Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.

CPEs and fixed versions use Anchore's correction of NVD's data: Added CPE configurations because not yet analyzed by NVD.