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CVE-2026-100650

HIGH 7.1

Published 2026-09-26 · Last modified 2026-10-01

vLLM through 0.29.0 fetches and fully materializes remote or inline media before enforcing its documented media controls (the VLLM_MAX_AUDIO_CLIP_FILESIZE_MB compressed-audio size cap, default 25 MB, and the per-modality --limit-mm-per-prompt item limits). Across four ingress paths — the shared media-acquisition layer (HTTPConnection.get_bytes()/async_get_bytes()), the chat completions audio_url/base64 path, the batch speech runner, and the Rust frontend POST /tokenize route — the server reads the entire HTTP response body, base64-decodes the inline payload, or spawns one fetch/decode task per media part, and only then applies the limit (or, on some paths, never applies it). A remote attacker can therefore cause the API server or batch-runner process to allocate memory and consume outbound bandwidth proportional to an attacker-chosen body size or media item count before the request is rejected, resulting in pre-inference memory and bandwidth exhaustion (denial of service). The chat and batch surfaces require an API key when one is configured; the Rust frontend /tokenize route is unauthenticated by design. There is no code execution or data disclosure impact.

ELEVATED IMPACT

Severe if exploited (CVSS 7.1), but no known exploitation and low modeled probability. Patch on a normal cadence.

Exploitation likelihood

0.8%chance of exploitation in 30 days · 57th percentile

○ In CISA KEV ○ Catalogued PoC

Impact if exploited

7.1CVSS 4.0 · HIGH

  • 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
  • ✓Requirements: No special attack requirements

✓ 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

CVSS vector

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

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): 0.30.0
    97 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, 0.28.0, 0.29.0, 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
    {
  "ecosystem": {},
  "database": {
    "source": "https://github.com/pypa/advisory-database/blob/main/vulns/vllm/PYSEC-2026-4184.yaml"
  }
}
    PyPA · PYSEC-2026-4184 · Updated 2026-10-07T10:00:03.384020476Z

Vendor product assessments

Showing 32 of 347 vendor product statements; 315 omitted. Read the full vendor advisory.

  • registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:f8a692451f509d11bf166288d614660cf8c15bab7494a822276bf458cfeb757a_amd64 as a component of Red Hat AI Inference Server 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-cpu-rhel9@sha256%3Af8a692451f509d11bf166288d614660cf8c15bab7494a822276bf458cfeb757a?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-cpu-rhel9&tag=1791857301

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:83112 Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d8d762cc0a3bb9782fdb32ddebfb2756d79ebed8e2964ee910a9ece352afa03a_arm64 as a component of Red Hat AI Inference Server 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-cuda-rhel9@sha256%3Ad8d762cc0a3bb9782fdb32ddebfb2756d79ebed8e2964ee910a9ece352afa03a?arch=arm64&repository_url=registry.redhat.io/rhaii/vllm-cuda-rhel9&tag=1791857183

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:83120 Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:c0e94a509b36a872b8b80a1578393f164a502d0d564ed696dd6a09001348b72d_ppc64le as a component of Red Hat AI Inference Server 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/vllm-spyre-rhel9@sha256%3Ac0e94a509b36a872b8b80a1578393f164a502d0d564ed696dd6a09001348b72d?arch=ppc64le&repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9&tag=1791858592

    • vendor fix: For more information visit https://access.redhat.com/errata/RHSA-2026:83114 Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:18eb52b8f8ed403671396e49370216a8a553d79dfe33efb3cae79284eb40643a_ppc64le as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-agent-rhel9@sha256%3A18eb52b8f8ed403671396e49370216a8a553d79dfe33efb3cae79284eb40643a?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9&tag=1791468381

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:636ba382740d2c14ace1c03d8474e460dc78929237e1efae7bd2da58605fb9f8_s390x as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-agent-rhel9@sha256%3A636ba382740d2c14ace1c03d8474e460dc78929237e1efae7bd2da58605fb9f8?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9&tag=1791468381

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:51997b1a7e5e2f3c0bd76b7d23fda33902843addafcbba6333b27d5c72beb5c4_amd64 as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-controller-rhel9@sha256%3A51997b1a7e5e2f3c0bd76b7d23fda33902843addafcbba6333b27d5c72beb5c4?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9&tag=1791468368

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:a2967b5e30b4b8c6299abde8dc0fcab8ceb15581aff94dda4f02ac6e6aa05cb7_s390x as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-controller-rhel9@sha256%3Aa2967b5e30b4b8c6299abde8dc0fcab8ceb15581aff94dda4f02ac6e6aa05cb7?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9&tag=1791468368

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:24cf45f8cc50e378b6a7ecb0f42568f6426242af8b2751d60350d04b3817ccd4_s390x as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-router-rhel9@sha256%3A24cf45f8cc50e378b6a7ecb0f42568f6426242af8b2751d60350d04b3817ccd4?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9&tag=1791468401

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:b165fd482192f769e5788603f3fbd34144aaf64121358f5e66e8327d4d35fb43_amd64 as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-router-rhel9@sha256%3Ab165fd482192f769e5788603f3fbd34144aaf64121358f5e66e8327d4d35fb43?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9&tag=1791468401

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:2ab213b45e46bccd7c9287a9b402100d46d25e9e58118ae57864513de58e77ca_ppc64le as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-storage-initializer-rhel9@sha256%3A2ab213b45e46bccd7c9287a9b402100d46d25e9e58118ae57864513de58e77ca?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9&tag=1790958549

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:856c7748eb45dbd190fb16fe90a09546ac8febf2f83e41d8068e8ac759442018_amd64 as a component of Red Hat OpenShift AI 3.4 · fixed

    Vendor assessment for this product only.

    pkg:oci/odh-kserve-storage-initializer-rhel9@sha256%3A856c7748eb45dbd190fb16fe90a09546ac8febf2f83e41d8068e8ac759442018?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9&tag=1790958549

    • vendor fix: For Red Hat OpenShift AI 3.4.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/ Vendor guidance
    • workaround: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • none available: Affected
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • none available: Affected
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • none available: Affected
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • none available: Affected
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • none available: Affected
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+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: Limit network exposure to the vLLM inference service and block outbound network connections from the serving host or container to prevent fetching untrusted external media: 1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000): # firewall-cmd --zone=public --remove-port=8000/tcp --permanent # firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent # firewall-cmd --reload 2. In OpenShift AI environments, apply a Kubernetes NetworkPolicy to the pod namespace to restrict ingress traffic strictly to authorized proxy pods, and apply an egress rule that blocks outbound internet access, preventing the service from downloading arbitrary remote media URLs. 3. Enforce maximum request body size limits on upstream reverse proxies or ingress controllers to drop oversized inline base64 payloads before forwarding requests to the inference backend. Caveats: Restricting egress traffic will prevent valid remote media references from being processed if external multi-modal inputs are expected. Modifying firewall rules or reloading network services may briefly interrupt active connections.
    • no fix planned: Will not fix
    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-ai-gateway-payload-processing-rhel9@sha256:1edfd89a924a83901845cd773012a3aeca70c09a2175a87e83351140efc80485_ppc64le as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-ai-gateway-payload-processing-rhel9@sha256%3A1edfd89a924a83901845cd773012a3aeca70c09a2175a87e83351140efc80485?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-ai-gateway-payload-processing-rhel9&tag=1791860396

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-eval-hub-rhel9@sha256:cb8f1ce5d2c2ae7a10d841837c7b18d3856f66869fc45d7bc8b4b193a4edbca8_arm64 as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-eval-hub-rhel9@sha256%3Acb8f1ce5d2c2ae7a10d841837c7b18d3856f66869fc45d7bc8b4b193a4edbca8?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-eval-hub-rhel9&tag=1791852583

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-llama-stack-core-rhel9@sha256:0aa47e4978f9ff14ca7e1341d4b26621cb8c271f0dff6fc8324c72afa5dbd17b_arm64 as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-llama-stack-core-rhel9@sha256%3A0aa47e4978f9ff14ca7e1341d4b26621cb8c271f0dff6fc8324c72afa5dbd17b?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-llama-stack-core-rhel9&tag=1790958470

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-maas-controller-rhel9@sha256:e305c12082dbed64798cc8ce855362f5703559f141308a7a0523f94d13886107_ppc64le as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-maas-controller-rhel9@sha256%3Ae305c12082dbed64798cc8ce855362f5703559f141308a7a0523f94d13886107?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-maas-controller-rhel9&tag=1791790095

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-mod-arch-autorag-rhel9@sha256:72b8f8d8a63c0664a729ec7afbfaa3e9ddb41d299253ed110b7358a91f55d144_ppc64le as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-mod-arch-autorag-rhel9@sha256%3A72b8f8d8a63c0664a729ec7afbfaa3e9ddb41d299253ed110b7358a91f55d144?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-mod-arch-autorag-rhel9&tag=1791860994

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-model-performance-data-rhel9@sha256:4ce3053cb04fdcdb6e2dfc5ba8f51cbd78d0d32c83f9e66ce376d273a592080f_ppc64le as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-model-performance-data-rhel9@sha256%3A4ce3053cb04fdcdb6e2dfc5ba8f51cbd78d0d32c83f9e66ce376d273a592080f?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-model-performance-data-rhel9&tag=1790958081

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9@sha256:a320d0547d3267d7ce0a3e004a44aed47d91b885ffe9840a65715e32a08ae0ec_arm64 as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-pipeline-runtime-minimal-cpu-py312-rhel9@sha256%3Aa320d0547d3267d7ce0a3e004a44aed47d91b885ffe9840a65715e32a08ae0ec?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-pipeline-runtime-minimal-cpu-py312-rhel9&tag=1791829069

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:7e0fb9bebdad1ebdef398243022fbfb29f6bd5673c00c4635745e0a92545ab60_arm64 as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-th06-cuda130-torch210-py312-rhel9@sha256%3A7e0fb9bebdad1ebdef398243022fbfb29f6bd5673c00c4635745e0a92545ab60?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9&tag=1791961113

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • registry.redhat.io/rhoai/odh-trustyai-vllm-orchestrator-gateway-rhel9@sha256:7b75fd02b3628b485fa58e89db548116c941941ec5da885767357e752bd03dd5_ppc64le as a component of Red Hat OpenShift AI 3.4 · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not present

    pkg:oci/odh-trustyai-vllm-orchestrator-gateway-rhel9@sha256%3A7b75fd02b3628b485fa58e89db548116c941941ec5da885767357e752bd03dd5?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-trustyai-vllm-orchestrator-gateway-rhel9&tag=1791363252

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00
  • rhoai/odh-th-torch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · known not affected

    Vendor assessment for this product only.

    Reason: vulnerable code not in execute path

    pkg:oci/odh-th-torch-cuda-py312-rhel9@sha256:efd7b3607b699092c68359d20ef0d0043114191a097637667c0d2cf78fbea702?repository_url=registry.redhat.io/rhoai/odh-th-torch-cuda-py312-rhel9

    Red Hat · CVE-2026-100650 · Updated 2026-10-19T11:32:22+00:00

Known Affected Software Configurations

VendorProductVersion range
VllmVllm≤ 0.29.0

All CVSS metrics

  • HIGH 7.1 v4.0 · CNA Primary
    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
  • 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
  • HIGH 7.1 v4.0 · NVD Secondary
    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/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
  • 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