CVE-2026-5497 (GCVE-0-2026-5497)
Vulnerability from cvelistv5 – Published: 2026-06-11 08:31 – Updated: 2026-07-03 12:04
VLAI
EPSS
VEX
Title
Unbounded Frame Count in video/jpeg Base64 Data URL Processing Leads to OOM DoS in vllm-project/vllm
Summary
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.
Severity
7.5 (High)
7.5 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://huntr.com/bounties/7bd92629-b396-4449-8f8… | |
| https://github.com/vllm-project/vllm/commit/58ee6… | |
| https://access.redhat.com/security/cve/CVE-2026-5497 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2487813 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
Impacted products
4 products
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm-project/vllm |
Affected:
unspecified , < 0.19.0
(custom)
|
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3 |
|
| Red Hat | Red Hat Enterprise Linux AI (RHEL AI) 3 |
cpe:/a:redhat:enterprise_linux_ai:3 |
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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