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CVE-2026-100649

MEDIUM 6.3

Published 2026-09-26 · Last modified 2026-10-02

vLLM before 0.29.0 contains a resource-limit bypass vulnerability in PyNvVideoCodec decoder allocation where sampler subclass shadowing allows independent counter increments. Unauthenticated attackers can select different sampler subclasses in video requests to exceed configured decoder limits and exhaust unaccounted GPU memory.

NO EXPLOITATION SIGNALS

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

Exploitation likelihood

0.3%chance of exploitation in 30 days · 27th percentile

○ In CISA KEV ○ Catalogued PoC

Impact if exploited

6.3CVSS 4.0 · MEDIUM

  • ConfidentialityNone
  • IntegrityNone
  • AvailabilityLow

What an attacker needs

  • ✓Access: Reachable over the network — no local access needed
  • ✓Privileges: No account or privileges required
  • ✓User interaction: No user interaction needed
  • ⚠Complexity: Needs a race window or specific setup
  • ⚠Requirements: Specific conditions must be present

✓ 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-770: Allocation of Resources Without Limits or Throttling

CVSS vector

CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

References and research data (JSON)

Vendor product assessments

  • rhaii/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@sha256:81c5994dc20321f8bd3969099eade78ef127cbd79f2d0ccea101c773e4c7fa4d?repository_url=registry.redhat.io/rhaii/vllm-cpu-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+00:00
  • rhaii/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@sha256:0d22e2301ce26ef68effda5d3a4a20381ad0ea34d1240e159e245bd44c2e125b?repository_url=registry.redhat.io/rhaii/vllm-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+00:00
  • rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server · known affected

    Vendor assessment for this product only.

    pkg:oci/vllm-gaudi-rhel9@sha256:ce1e4fd82dd37d299ce8c64a237971d00faf602f06868c986e81aefa04c283ef?repository_url=registry.redhat.io/rhaii/vllm-gaudi-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:1079c9afecdbc33f4790792f0d712dbf7523790a92dcfbe8fb5cf4c2d282fc5b?repository_url=registry.redhat.io/rhaii/vllm-neuron-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+00:00
  • rhaii/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@sha256:1a30359d6ca5d0891e403b9b0a23c0ae1ad6e30a2429b9b42e246b874fe8859a?repository_url=registry.redhat.io/rhaii/vllm-rocm-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+00:00
  • rhaii/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@sha256:11ad6e13c41728944d933c7abe01d1d10ecb3f8e8b4f0da50ec53b7df6f036a8?repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:0c2ac747d67dc0e80162ff05867dd702e474b2217659f19f28b8e3a0c03a942a?repository_url=registry.redhat.io/rhaii/vllm-tpu-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:fb7791bbfea99e9f81529fe8cc1e7c834ca470585c27590e3d0a6834167cd436?repository_url=registry.redhat.io/rhaiis/vllm-cpu-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:73bbc4560132c4aaa9bbd608ce9560eeae7b30bcc8f014da460afa5622139372?repository_url=registry.redhat.io/rhaiis/vllm-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:8ba72cec5d79b1a751eb09774c81fd6181b43ec69a2e5116982661b34366874d?repository_url=registry.redhat.io/rhaiis/vllm-neuron-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:37b64a8df90756aae0ee96d2f07765adbce5ed5beb2414ac3845806ad2219cfc?repository_url=registry.redhat.io/rhaiis/vllm-rocm-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:3dd7eff1c62cd4609b4c76590b77bb801cd064e40642ed32b5aa900a6bc643ea?repository_url=registry.redhat.io/rhaiis/vllm-spyre-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:b1c12112a1e8a298645e10d34d44c9961a6121aa8d87fc5d65ad83e1ffa3c586?repository_url=registry.redhat.io/rhaiis/vllm-tpu-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:a754d4d1126e98f203249414b3831182bb15ee6b3643452f167849defc642e4b?repository_url=registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:df5b6edd1bdb055219a7385b6d5923f3262c3a408f92da734e3027af55fd593f?repository_url=registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:8ea1233b1fb6b82d0aa69e9e4c47d2df242001280c11a61584066c27c5bec3a5?repository_url=registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:b45f3bc25a7d3c9f3bfdc1de3be6db59f6c3109f62326d74fae14e832a797981?repository_url=registry.redhat.io/rhelai3/bootc-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:e1057b59ca1be9501332abf97230855e85c7df858356be0da9dce230ea7e98e1?repository_url=registry.redhat.io/rhelai3/bootc-gaudi-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:bac5d435d156c3ddcb581d3ff05fdce9f72bd617c5b8435cede16bb17b7bf12e?repository_url=registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:38ce057f97034bbb8eaea574d41cc7eebaeaa9265a4cdd966476dd3fac129c3c?repository_url=registry.redhat.io/rhelai3/bootc-rocm-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:eddf436baf8906d08f1cd28fecdc73f70f9bbf11a73e327d9c97495ee921c523?repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:8b4d4c6a45b2ff1b25fda45dc022066ed5d055267ce08ab0b31e58191970632e?repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:1fb5c0b39c43904a5843a9e01cddac40cfe5580d9bb7abdf6083c9f53d1655b3?repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:7c88948bcfa0fc3439b1eae31c426639346044505aff440238bfb194fd15ce99?repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:cb5eedeefd2578cd9bd8637250bbe299c84539f57b4ead75d630ed9f833a6ef2?repository_url=registry.redhat.io/rhoai/odh-llm-d-kv-cache-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:efd7b3607b699092c68359d20ef0d0043114191a097637667c0d2cf78fbea702?repository_url=registry.redhat.io/rhoai/odh-th-torch-cuda-py312-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+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@sha256:87eec3862eb6ddef445f89dcf6fd89add7ef202ea7c563b1ee7347537fb3e250?repository_url=registry.redhat.io/rhoai/odh-vllm-gaudi-rhel9

    • workaround: 1. Summary of action: Restrict network reachability to the inference server and avoid deploying models with the hardware-accelerated PyNvVideoCodec video backend. 2. Command examples: To block direct untrusted network access to the vLLM server port (default 8000) using firewalld: ``` firewall-cmd --permanent --zone=public --remove-port=8000/tcp firewall-cmd --permanent --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> firewall-cmd --reload ``` In OpenShift environments, deploy a NetworkPolicy to restrict ingress traffic so that only authenticated API gateways or designated client pods can reach the inference service. Additionally, avoid specifying "backend": "pynvvideocodec" in the server video configuration. 3. Caveats: Restricting network access prevents direct unauthenticated requests from reaching the endpoint. Avoiding hardware-accelerated video decoding shifts media decoding workloads to software, which may increase CPU utilization and inference latency for video inputs. 4. Warning: Applying firewall rule reloads takes effect immediately and may sever existing connections. Modifying deployment configurations or restarting inference pods will temporarily interrupt ongoing serving sessions.
    • none available: Fix deferred
    Red Hat · CVE-2026-100649 · Updated 2026-10-06T23:36:00+00:00

Known Affected Software Configurations

VendorProductVersion range
VllmVllm< 0.29.0

All CVSS metrics

  • MEDIUM 6.3 v4.0 · CNA Primary
    CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
  • LOW 3.7 v3.1 · CNA Primary
    CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L
  • MEDIUM 6.3 v4.0 · NVD Secondary
    CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
  • LOW 3.7 v3.1 · NVD Secondary
    CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L
  • MEDIUM 5.3 v3.1 · NVD Primary
    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L

References

Advisories

Technical & other

Sources: NVD · CVE.org · EPSS