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CVE-2026-69147

MEDIUM 6.5

Published 2026-09-16 · Last modified 2026-09-16

vLLM is an inference and serving engine for large language models. Prior to 0.28.0, request bodies for Chat Completions and Responses can set media_io_kwargs.video.video_backend to pynvvideocodec, and MediaConnector.fetch_video forwards that choice to VideoMediaIO even when startup configuration selected a software decoder. The engine's _reserve_mm_ipc_gpu_memory logic budgets decoder memory only from static configuration, so the request-selected VIDEO_LOADER_REGISTRY backend can create a CUDA context, decoder surfaces, and decoded-frame allocations that were not removed from the engine's KV-cache budget. An attacker able to submit video requests to a video-capable GPU deployment with PyNvVideoCodec installed can exhaust shared GPU memory, causing request failures, worker crashes, or denial of service. The first release containing the fix is version 0.28.0.

NO EXPLOITATION SIGNALS

No known exploitation, public exploit, or elevated probability at this time. Track for changes.

Exploitation likelihood

0.6%chance of exploitation in 30 days · 48th percentile

○ In CISA KEV ○ Catalogued PoC

Impact if exploited

6.5CVSS 3.1 · MEDIUM

  • ConfidentialityNone
  • IntegrityNone
  • AvailabilityHigh

What an attacker needs

  • ✓Access: Reachable over the network — no local access needed
  • ⚠Privileges: Requires a low-privilege account
  • ✓User interaction: No user interaction needed
  • ✓Complexity: No special conditions — reliably repeatable

✓ lowers the bar for an attacker · ⚠ raises it

Proof of concept

No public PoC is currently catalogued.

Search for new PoCs

Search GitHub now — results may be empty or unverified.

Tracked sources are checked daily for recent CVEs, including PoCs published after the original disclosure. A later release appears after a tracked source lists it and a refresh succeeds.

Data snapshot:

Affected

Vendors Vllm Project Vllm

Products Vllm

Weakness (CWE)

  • CWE-400: : Uncontrolled Resource Consumption
  • CWE-770: : Allocation of Resources Without Limits or Throttling

CVSS vector

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

References and research data (JSON)

Packages and version ranges

Each advisory describes its own package and affected ranges. A boundary applies to that range; it is not a universal upgrade recommendation.

  • vllm PyPI

    pkg:pypi/vllm

    ECOSYSTEM range
    • All earlier versions (introduced 0)
    • Fixed boundary (excluded): 0.28.0
    95 explicitly listed affected versions

    0.0.1, 0.1.0, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.10.0, 0.10.1, 0.10.1.1, 0.10.2, 0.11.0, 0.11.1, 0.11.2, 0.12.0, 0.13.0, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.16.0, 0.17.0, 0.17.1, 0.18.0, 0.18.1, 0.19.0, 0.19.1, 0.2.0, 0.2.1, 0.2.1.post1, 0.2.2, 0.2.3, 0.2.4, 0.2.5, 0.2.6, 0.2.7, 0.20.0, 0.20.1, 0.20.2, 0.21.0, 0.22.0, 0.22.1, 0.23.0, 0.24.0, 0.25.0, 0.25.1, 0.26.0, 0.27.0, 0.27.1, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.4.0, 0.4.0.post1, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.5.0.post1, 0.5.1, 0.5.2, 0.5.3, 0.5.3.post1, 0.5.4, 0.5.5, 0.6.0, 0.6.1, 0.6.1.post1, 0.6.1.post2, 0.6.2, 0.6.3, 0.6.3.post1, 0.6.4, 0.6.4.post1, 0.6.5, 0.6.6, 0.6.6.post1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0, 0.8.1, 0.8.2, 0.8.3, 0.8.4, 0.8.5, 0.8.5.post1, 0.9.0, 0.9.0.1, 0.9.1, 0.9.2

    Additional upstream package constraints
    {
  "database": {
    "source": "https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2026/09/GHSA-8pw2-6jv3-mj5j/GHSA-8pw2-6jv3-mj5j.json"
  }
}
    GitHub Advisory Database · GHSA-8pw2-6jv3-mj5j · Updated 2026-10-01T17:55:52.391144765Z
  • vllm PyPI

    pkg:pypi/vllm

    ECOSYSTEM range
    • All earlier versions (introduced 0)
    • Fixed boundary (excluded): 0.28.0
    95 explicitly listed affected versions

    0.0.1, 0.1.0, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.10.0, 0.10.1, 0.10.1.1, 0.10.2, 0.11.0, 0.11.1, 0.11.2, 0.12.0, 0.13.0, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.16.0, 0.17.0, 0.17.1, 0.18.0, 0.18.1, 0.19.0, 0.19.1, 0.2.0, 0.2.1, 0.2.1.post1, 0.2.2, 0.2.3, 0.2.4, 0.2.5, 0.2.6, 0.2.7, 0.20.0, 0.20.1, 0.20.2, 0.21.0, 0.22.0, 0.22.1, 0.23.0, 0.24.0, 0.25.0, 0.25.1, 0.26.0, 0.27.0, 0.27.1, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.4.0, 0.4.0.post1, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.5.0.post1, 0.5.1, 0.5.2, 0.5.3, 0.5.3.post1, 0.5.4, 0.5.5, 0.6.0, 0.6.1, 0.6.1.post1, 0.6.1.post2, 0.6.2, 0.6.3, 0.6.3.post1, 0.6.4, 0.6.4.post1, 0.6.5, 0.6.6, 0.6.6.post1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0, 0.8.1, 0.8.2, 0.8.3, 0.8.4, 0.8.5, 0.8.5.post1, 0.9.0, 0.9.0.1, 0.9.1, 0.9.2

    Additional upstream package constraints
    {
  "database": {
    "source": "https://github.com/pypa/advisory-database/blob/main/vulns/vllm/PYSEC-2026-4178.yaml"
  }
}
    PyPA · PYSEC-2026-4178 · Updated 2026-10-01T17:45:09.325879460Z

Vendor product assessments

  • registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:bcd8563d0888d97cb346557d77f823ba8480daa84695e2bfb9e2e1827117fb39_amd64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-cpu-rhel9@sha256%3Abcd8563d0888d97cb346557d77f823ba8480daa84695e2bfb9e2e1827117fb39?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-cpu-rhel9&tag=1791473614

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82415 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:57c9bb9052ad8ab90348b91fa7d59c6e5f02311cf812e89345cdbfd6da286280_arm64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-cuda-rhel9@sha256%3A57c9bb9052ad8ab90348b91fa7d59c6e5f02311cf812e89345cdbfd6da286280?arch=arm64&repository_url=registry.redhat.io/rhaii/vllm-cuda-rhel9&tag=1791473631

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82368 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:8d8180e8c11b6ab6344ad42ef80ed03e35815d2fcd835b3b4ec931c64b934461_amd64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-cuda-rhel9@sha256%3A8d8180e8c11b6ab6344ad42ef80ed03e35815d2fcd835b3b4ec931c64b934461?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-cuda-rhel9&tag=1791473631

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82368 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-gaudi-rhel9@sha256:ba46859d4cb085c111a27bc665c15e5f826320e86c03b940f7a28f40df79ad22_amd64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-gaudi-rhel9@sha256%3Aba46859d4cb085c111a27bc665c15e5f826320e86c03b940f7a28f40df79ad22?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-gaudi-rhel9&tag=1791427182

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82061 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:fad3819d9130fa3e9cfa7ab2f0d1b420140d93cfe2242f78dc289146ef985f4e_amd64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-rocm-rhel9@sha256%3Afad3819d9130fa3e9cfa7ab2f0d1b420140d93cfe2242f78dc289146ef985f4e?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-rocm-rhel9&tag=1791425869

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82059 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:06d05633adc879bab2f1e3e7b3b394873304f4dd520e0f2a0b84da344e9bda30_amd64 as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-spyre-rhel9@sha256%3A06d05633adc879bab2f1e3e7b3b394873304f4dd520e0f2a0b84da344e9bda30?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9&tag=1791818357

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82371 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:0e3d5aab4d8a75370eca1e71854fa31b9cd7e219b6a5e990d9b559b17045cb9d_s390x as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-spyre-rhel9@sha256%3A0e3d5aab4d8a75370eca1e71854fa31b9cd7e219b6a5e990d9b559b17045cb9d?arch=s390x&repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9&tag=1791818357

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82371 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:a692177742a9f381b5394f60c9fbb3856d59e1a6e86140ee980c5fd184fff137_ppc64le as a component of Red Hat AI Inference Server 3.5 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-spyre-rhel9@sha256%3Aa692177742a9f381b5394f60c9fbb3856d59e1a6e86140ee980c5fd184fff137?arch=ppc64le&repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9&tag=1791818357

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:82371 Vendor guidance
    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-neuron-rhel9?repository_url=registry.redhat.io/rhaii/vllm-neuron-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-tpu-rhel9?repository_url=registry.redhat.io/rhaii/vllm-tpu-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-cpu-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-cpu-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-cuda-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-cuda-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-neuron-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-neuron-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-rocm-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-rocm-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-spyre-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-spyre-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-tpu-rhel9?repository_url=registry.redhat.io/rhaiis/vllm-tpu-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-aws-cuda-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-azure-cuda-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-azure-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-azure-rocm-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-cuda-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-cuda-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-gaudi-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-gaudi-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-gcp-cuda-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3 · known affected

    Vendor assessment for this product only.

    pkg:oci/bootc-rocm-rhel9?repository_url=registry.redhat.io/rhelai3/bootc-rocm-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-agent-rhel9?repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-controller-rhel9?repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-router-rhel9?repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-storage-initializer-rhel9?repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-llm-d-kv-cache-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-llm-d-kv-cache-rhel9?repository_url=registry.redhat.io/rhoai/odh-llm-d-kv-cache-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-th-torch-cuda-py312-rhel9?repository_url=registry.redhat.io/rhoai/odh-th-torch-cuda-py312-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00
  • rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known affected

    Vendor assessment for this product only.

    pkg:oci/odh-vllm-gaudi-rhel9?repository_url=registry.redhat.io/rhoai/odh-vllm-gaudi-rhel9

    • workaround: Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability. The upstream vLLM project addressed this flaw in version 0.28.0, while fixes for Red Hat AI Inference Server and Red Hat OpenShift AI are currently deferred. Exposure can be reduced by not installing the PyNvVideoCodec decoder in vLLM serving images, since exploitation requires that backend to be present.
    • none available: Fix deferred
    Red Hat · CVE-2026-69147 · Updated 2026-10-16T11:53:51+00:00

Known Affected Software Configurations

VendorProductVersion range
VllmVllm< 0.28.0

All CVSS metrics

  • MEDIUM 6.5 v3.1 · CNA Primary
    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
  • MEDIUM 6.5 v3.1 · NVD Secondary
    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

References

Exploit-tagged references (not proof of a PoC)

Patches & mitigations

Advisories

Technical & other

Sources: NVD · CVE.org · EPSS