Vulnerabilities
Misconfiguration
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CVE Vulnerabilities

CVE-2026-5497

Uncontrolled Resource Consumption

Published: Jun 11, 2026 | Modified: Jul 22, 2026
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
7.5 IMPORTANT
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Ubuntu
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Additional information
NVDhttps://nvd.nist.gov/vuln/detail/CVE-2026-5497
CWEhttps://cwe.mitre.org/data/definitions/400.html

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.

Weakness

The product does not properly control the allocation and maintenance of a limited resource.

Affected Software

NameVendorStart VersionEnd Version
VllmVllm0.8.0 (including)0.19.0 (excluding)
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-spyre-rhel9:1789681201*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-cpu-rhel9:1789681128*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-cuda-rhel9:1789681126*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-rocm-rhel9:1789681126*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-cpu-rhel9:1790075793*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-spyre-rhel9:1790076141*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-cuda-rhel9:1790090131*
Red Hat AI Inference Server 3.4RedHatrhaii/vllm-rocm-rhel9:1790109620*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/disk-image-cuda-rhel9:1790957417*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-aws-cuda-rhel9:1790881122*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-azure-cuda-rhel9:1790881120*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-azure-rocm-rhel9:1790707552*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-cuda-rhel9:1790868254*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-gcp-cuda-rhel9:1790881123*
Red Hat Enterprise Linux AI 3.4RedHatrhelai3/bootc-rocm-rhel9:1790614447*

Potential Mitigations

  • Mitigation of resource exhaustion attacks requires that the target system either:

  • The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question.

  • The second solution is simply difficult to effectively institute – and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.

References

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