CVE-2026-5497
June 11, 2026, 10:16 a.m.
7.5
High
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.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Vllm |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-400
Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | vllm | vllm | 0.8.0-* | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.0
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: NONE
- Scope: UNCHANGED
- Confidentiality Impact: NONE
- Integrity Impact: NONE
- Availability Impact: HIGH
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Timeline
Published: June 11, 2026, 10:16 a.m.
Last Modified: June 11, 2026, 10:16 a.m.
Last Modified: June 11, 2026, 10:16 a.m.
Status : Received
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.