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This CVE record has been updated after NVD enrichment efforts were completed. Enrichment data supplied by the NVD may require amendment due to these changes.
Description
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
Metrics
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[{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI 3.4","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3.4::el9"]},{"vendor":"Red Hat","product":"Red Hat AI Inference Server","defaultStatus":"affected","cpes":["cpe:/a:redhat:ai_inference_server:3"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI) 3","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","defaultStatus":"affected","cpes":["cpe:/a:redhat:openshift_ai"]}]
[{"vendor":"Red Hat","product":"Red Hat AI Inference Server","defaultStatus":"affected","cpes":["cpe:/a:redhat:ai_inference_server:3"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI) 3","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","defaultStatus":"affected","cpes":["cpe:/a:redhat:openshift_ai"]}]
CVE Modified by redhat-SADP7/01/2026 9:17:48 AM
Action
Type
Old Value
New Value
Added
Reference
https://access.redhat.com/errata/RHSA-2026:33524
Added
Reference
https://access.redhat.com/errata/RHSA-2026:33531
Changed
Affected
[{"vendor":"Red Hat","product":"Red Hat AI Inference Server","defaultStatus":"affected","cpes":["cpe:/a:redhat:ai_inference_server:3"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI) 3","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","defaultStatus":"affected","cpes":["cpe:/a:redhat:openshift_ai"]}]
[{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI 3.4","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3.4::el9"]},{"vendor":"Red Hat","product":"Red Hat AI Inference Server","defaultStatus":"affected","cpes":["cpe:/a:redhat:ai_inference_server:3"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI) 3","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","defaultStatus":"affected","cpes":["cpe:/a:redhat:openshift_ai"]}]
[{"vendor":"Red Hat","product":"Red Hat AI Inference Server","defaultStatus":"affected","cpes":["cpe:/a:redhat:ai_inference_server:3"]},{"vendor":"Red Hat","product":"Red Hat Enterprise Linux AI (RHEL AI) 3","defaultStatus":"affected","cpes":["cpe:/a:redhat:enterprise_linux_ai:3"]},{"vendor":"Red Hat","product":"Red Hat OpenShift AI (RHOAI)","defaultStatus":"affected","cpes":["cpe:/a:redhat:openshift_ai"]}]
New CVE Received from huntr.dev6/11/2026 6:16:21 AM
Action
Type
Old Value
New Value
Added
Description
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
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