Medium severity · published Apr 6, 2026
CVE-2026-34755
vLLM Affected by Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing
6.5
CVSS
0.5%
EPSS
No
On KEV
Description
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.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Confidentiality
None
Integrity
None
Availability
High
Scope
Unchanged
Exploitation
Is it being used?
Exploit prediction (EPSS)0.5% · higher than 40% of CVEs
Exploited in the wildNo evidence yet
CISA KEVNot listed
Ransomware campaign useNot known
Affected products
Which versions
VL
vllm · vllm-project
>= 0.7.0, < 0.19.0 · source: vendor, NVD
RH
Red Hat AI Inference Server · Red Hat
All versions, or not specified · source: CISA
RH
Red Hat AI Inference Server 3.2 · Red Hat
All versions, or not specified · source: CISA
RH
Red Hat AI Inference Server 3.4 · Red Hat
All versions, or not specified · source: CISA
RH
Red Hat Enterprise Linux AI 3.4 · Red Hat
All versions, or not specified · source: CISA
RH
Red Hat OpenShift AI (RHOAI) · Red Hat
All versions, or not specified · source: CISA
Affected product
VL
vllm
vllm-project
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