{"schema":"vulnpedia.research/1","cve":"CVE-2026-100650","generated":"2026-10-26T13:52:32.101Z","references":[{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-p6g9-7v3x-m8mv","tags":["vendor-advisory","exploit","Exploit","Mitigation","Vendor 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enisa.europa.eu/vulnerability/EUVD-2026-87744","tags":["ADVISORY"],"origins":[{"source":"euvd","sourceId":"EUVD-2026-87744","url":"https://euvd.enisa.europa.eu/vulnerability/EUVD-2026-87744"}]}],"packages":[{"source":"pysec","sourceId":"PYSEC-2026-4184","url":"https://osv.dev/vulnerability/PYSEC-2026-4184","ecosystem":"PyPI","name":"vllm","purl":"pkg:pypi/vllm","ranges":[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"0.30.0"}]}],"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"],"ecosystemSpecific":{},"databaseSpecific":{"source":"https://github.com/pypa/advisory-database/blob/main/vulns/vllm/PYSEC-2026-4184.yaml"},"modified":"2026-10-07T10:00:03.384020476Z","origins":[{"source":"pysec","sourceId":"PYSEC-2026-4184","modified":"2026-10-07T10:00:03.384020476Z","url":"https://osv.dev/vulnerability/PYSEC-2026-4184"}]}],"pocCandidates":[],"cataloguedPoc":[],"vendorStatements":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:f8a692451f509d11bf166288d614660cf8c15bab7494a822276bf458cfeb757a_amd64","product":"registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:f8a692451f509d11bf166288d614660cf8c15bab7494a822276bf458cfeb757a_amd64 as a component of Red Hat AI Inference Server 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"For more information visit https://access.redhat.com/errata/RHSA-2026:83112","url":"https://access.redhat.com/errata/RHSA-2026:83112"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/vllm-cpu-rhel9@sha256%3Af8a692451f509d11bf166288d614660cf8c15bab7494a822276bf458cfeb757a?arch=amd64&repository_url=registry.redhat.io/rhaii/vllm-cpu-rhel9&tag=1791857301","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d8d762cc0a3bb9782fdb32ddebfb2756d79ebed8e2964ee910a9ece352afa03a_arm64","product":"registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d8d762cc0a3bb9782fdb32ddebfb2756d79ebed8e2964ee910a9ece352afa03a_arm64 as a component of Red Hat AI Inference Server 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"For more information visit https://access.redhat.com/errata/RHSA-2026:83120","url":"https://access.redhat.com/errata/RHSA-2026:83120"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/vllm-cuda-rhel9@sha256%3Ad8d762cc0a3bb9782fdb32ddebfb2756d79ebed8e2964ee910a9ece352afa03a?arch=arm64&repository_url=registry.redhat.io/rhaii/vllm-cuda-rhel9&tag=1791857183","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:c0e94a509b36a872b8b80a1578393f164a502d0d564ed696dd6a09001348b72d_ppc64le","product":"registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:c0e94a509b36a872b8b80a1578393f164a502d0d564ed696dd6a09001348b72d_ppc64le as a component of Red Hat AI Inference Server 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"For more information visit https://access.redhat.com/errata/RHSA-2026:83114","url":"https://access.redhat.com/errata/RHSA-2026:83114"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/vllm-spyre-rhel9@sha256%3Ac0e94a509b36a872b8b80a1578393f164a502d0d564ed696dd6a09001348b72d?arch=ppc64le&repository_url=registry.redhat.io/rhaii/vllm-spyre-rhel9&tag=1791858592","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:18eb52b8f8ed403671396e49370216a8a553d79dfe33efb3cae79284eb40643a_ppc64le","product":"registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:18eb52b8f8ed403671396e49370216a8a553d79dfe33efb3cae79284eb40643a_ppc64le as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-agent-rhel9@sha256%3A18eb52b8f8ed403671396e49370216a8a553d79dfe33efb3cae79284eb40643a?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9&tag=1791468381","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:636ba382740d2c14ace1c03d8474e460dc78929237e1efae7bd2da58605fb9f8_s390x","product":"registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:636ba382740d2c14ace1c03d8474e460dc78929237e1efae7bd2da58605fb9f8_s390x as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-agent-rhel9@sha256%3A636ba382740d2c14ace1c03d8474e460dc78929237e1efae7bd2da58605fb9f8?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-agent-rhel9&tag=1791468381","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:51997b1a7e5e2f3c0bd76b7d23fda33902843addafcbba6333b27d5c72beb5c4_amd64","product":"registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:51997b1a7e5e2f3c0bd76b7d23fda33902843addafcbba6333b27d5c72beb5c4_amd64 as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-controller-rhel9@sha256%3A51997b1a7e5e2f3c0bd76b7d23fda33902843addafcbba6333b27d5c72beb5c4?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9&tag=1791468368","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:a2967b5e30b4b8c6299abde8dc0fcab8ceb15581aff94dda4f02ac6e6aa05cb7_s390x","product":"registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:a2967b5e30b4b8c6299abde8dc0fcab8ceb15581aff94dda4f02ac6e6aa05cb7_s390x as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-controller-rhel9@sha256%3Aa2967b5e30b4b8c6299abde8dc0fcab8ceb15581aff94dda4f02ac6e6aa05cb7?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-controller-rhel9&tag=1791468368","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:24cf45f8cc50e378b6a7ecb0f42568f6426242af8b2751d60350d04b3817ccd4_s390x","product":"registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:24cf45f8cc50e378b6a7ecb0f42568f6426242af8b2751d60350d04b3817ccd4_s390x as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-router-rhel9@sha256%3A24cf45f8cc50e378b6a7ecb0f42568f6426242af8b2751d60350d04b3817ccd4?arch=s390x&repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9&tag=1791468401","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:b165fd482192f769e5788603f3fbd34144aaf64121358f5e66e8327d4d35fb43_amd64","product":"registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:b165fd482192f769e5788603f3fbd34144aaf64121358f5e66e8327d4d35fb43_amd64 as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-router-rhel9@sha256%3Ab165fd482192f769e5788603f3fbd34144aaf64121358f5e66e8327d4d35fb43?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-router-rhel9&tag=1791468401","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:2ab213b45e46bccd7c9287a9b402100d46d25e9e58118ae57864513de58e77ca_ppc64le","product":"registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:2ab213b45e46bccd7c9287a9b402100d46d25e9e58118ae57864513de58e77ca_ppc64le as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-storage-initializer-rhel9@sha256%3A2ab213b45e46bccd7c9287a9b402100d46d25e9e58118ae57864513de58e77ca?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9&tag=1790958549","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:856c7748eb45dbd190fb16fe90a09546ac8febf2f83e41d8068e8ac759442018_amd64","product":"registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:856c7748eb45dbd190fb16fe90a09546ac8febf2f83e41d8068e8ac759442018_amd64 as a component of Red Hat OpenShift AI 3.4","status":"fixed","remediations":[{"category":"vendor_fix","details":"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:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/","url":"https://access.redhat.com/errata/RHSA-2026:84734"},{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."}],"purl":"pkg:oci/odh-kserve-storage-initializer-rhel9@sha256%3A856c7748eb45dbd190fb16fe90a09546ac8febf2f83e41d8068e8ac759442018?arch=amd64&repository_url=registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9&tag=1790958549","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaii/vllm-gaudi-rhel9","product":"rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"none_available","details":"Affected"}],"purl":"pkg:oci/vllm-gaudi-rhel9@sha256:ce1e4fd82dd37d299ce8c64a237971d00faf602f06868c986e81aefa04c283ef?repository_url=registry.redhat.io/rhaii/vllm-gaudi-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaii/vllm-tpu-rhel9","product":"rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/vllm-tpu-rhel9@sha256:0c2ac747d67dc0e80162ff05867dd702e474b2217659f19f28b8e3a0c03a942a?repository_url=registry.redhat.io/rhaii/vllm-tpu-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaiis/vllm-cpu-rhel9","product":"rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/vllm-cpu-rhel9@sha256:fb7791bbfea99e9f81529fe8cc1e7c834ca470585c27590e3d0a6834167cd436?repository_url=registry.redhat.io/rhaiis/vllm-cpu-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaiis/vllm-neuron-rhel9","product":"rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/vllm-neuron-rhel9@sha256:8ba72cec5d79b1a751eb09774c81fd6181b43ec69a2e5116982661b34366874d?repository_url=registry.redhat.io/rhaiis/vllm-neuron-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaiis/vllm-spyre-rhel9","product":"rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/vllm-spyre-rhel9@sha256:3dd7eff1c62cd4609b4c76590b77bb801cd064e40642ed32b5aa900a6bc643ea?repository_url=registry.redhat.io/rhaiis/vllm-spyre-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_ai_inference_server:rhaiis/vllm-tpu-rhel9","product":"rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/vllm-tpu-rhel9@sha256:b1c12112a1e8a298645e10d34d44c9961a6121aa8d87fc5d65ad83e1ffa3c586?repository_url=registry.redhat.io/rhaiis/vllm-tpu-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_enterprise_linux_ai_(rhel_ai)_3:rhelai3/bootc-azure-cuda-rhel9","product":"rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"none_available","details":"Affected"}],"purl":"pkg:oci/bootc-azure-cuda-rhel9@sha256:df5b6edd1bdb055219a7385b6d5923f3262c3a408f92da734e3027af55fd593f?repository_url=registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_enterprise_linux_ai_(rhel_ai)_3:rhelai3/bootc-cuda-rhel9","product":"rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"none_available","details":"Affected"}],"purl":"pkg:oci/bootc-cuda-rhel9@sha256:b45f3bc25a7d3c9f3bfdc1de3be6db59f6c3109f62326d74fae14e832a797981?repository_url=registry.redhat.io/rhelai3/bootc-cuda-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_enterprise_linux_ai_(rhel_ai)_3:rhelai3/bootc-gcp-cuda-rhel9","product":"rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"none_available","details":"Affected"}],"purl":"pkg:oci/bootc-gcp-cuda-rhel9@sha256:bac5d435d156c3ddcb581d3ff05fdce9f72bd617c5b8435cede16bb17b7bf12e?repository_url=registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_enterprise_linux_ai_(rhel_ai)_3:rhelai3/bootc-rocm-rhel9","product":"rhelai3/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"none_available","details":"Affected"}],"purl":"pkg:oci/bootc-rocm-rhel9@sha256:38ce057f97034bbb8eaea574d41cc7eebaeaa9265a4cdd966476dd3fac129c3c?repository_url=registry.redhat.io/rhelai3/bootc-rocm-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"red_hat_openshift_ai_(rhoai):rhoai/odh-vllm-gaudi-rhel9","product":"rhoai/odh-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI)","status":"known_affected","remediations":[{"category":"workaround","details":"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:\n\n1. Restrict inbound access using firewall rules so only trusted client networks or API gateways can reach the inference port (e.g., port 8000):\n# firewall-cmd --zone=public --remove-port=8000/tcp --permanent\n# firewall-cmd --zone=trusted --add-source=<TRUSTED_IP_OR_CIDR> --permanent\n# firewall-cmd --reload\n\n2. 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.\n\n3. 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.\n\nCaveats:\nRestricting 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."},{"category":"no_fix_planned","details":"Will not fix"}],"purl":"pkg:oci/odh-vllm-gaudi-rhel9@sha256:87eec3862eb6ddef445f89dcf6fd89add7ef202ea7c563b1ee7347537fb3e250?repository_url=registry.redhat.io/rhoai/odh-vllm-gaudi-rhel9","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-ai-gateway-payload-processing-rhel9@sha256:1edfd89a924a83901845cd773012a3aeca70c09a2175a87e83351140efc80485_ppc64le","product":"registry.redhat.io/rhoai/odh-ai-gateway-payload-processing-rhel9@sha256:1edfd89a924a83901845cd773012a3aeca70c09a2175a87e83351140efc80485_ppc64le as a component of Red Hat OpenShift AI 3.4","status":"known_not_affected","remediations":[],"justification":"vulnerable_code_not_present","purl":"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","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-eval-hub-rhel9@sha256:cb8f1ce5d2c2ae7a10d841837c7b18d3856f66869fc45d7bc8b4b193a4edbca8_arm64","product":"registry.redhat.io/rhoai/odh-eval-hub-rhel9@sha256:cb8f1ce5d2c2ae7a10d841837c7b18d3856f66869fc45d7bc8b4b193a4edbca8_arm64 as a component of Red Hat OpenShift AI 3.4","status":"known_not_affected","remediations":[],"justification":"vulnerable_code_not_present","purl":"pkg:oci/odh-eval-hub-rhel9@sha256%3Acb8f1ce5d2c2ae7a10d841837c7b18d3856f66869fc45d7bc8b4b193a4edbca8?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-eval-hub-rhel9&tag=1791852583","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-llama-stack-core-rhel9@sha256:0aa47e4978f9ff14ca7e1341d4b26621cb8c271f0dff6fc8324c72afa5dbd17b_arm64","product":"registry.redhat.io/rhoai/odh-llama-stack-core-rhel9@sha256:0aa47e4978f9ff14ca7e1341d4b26621cb8c271f0dff6fc8324c72afa5dbd17b_arm64 as a component of Red Hat OpenShift AI 3.4","status":"known_not_affected","remediations":[],"justification":"vulnerable_code_not_present","purl":"pkg:oci/odh-llama-stack-core-rhel9@sha256%3A0aa47e4978f9ff14ca7e1341d4b26621cb8c271f0dff6fc8324c72afa5dbd17b?arch=arm64&repository_url=registry.redhat.io/rhoai/odh-llama-stack-core-rhel9&tag=1790958470","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-maas-controller-rhel9@sha256:e305c12082dbed64798cc8ce855362f5703559f141308a7a0523f94d13886107_ppc64le","product":"registry.redhat.io/rhoai/odh-maas-controller-rhel9@sha256:e305c12082dbed64798cc8ce855362f5703559f141308a7a0523f94d13886107_ppc64le as a component of Red Hat OpenShift AI 3.4","status":"known_not_affected","remediations":[],"justification":"vulnerable_code_not_present","purl":"pkg:oci/odh-maas-controller-rhel9@sha256%3Ae305c12082dbed64798cc8ce855362f5703559f141308a7a0523f94d13886107?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-maas-controller-rhel9&tag=1791790095","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-mod-arch-autorag-rhel9@sha256:72b8f8d8a63c0664a729ec7afbfaa3e9ddb41d299253ed110b7358a91f55d144_ppc64le","product":"registry.redhat.io/rhoai/odh-mod-arch-autorag-rhel9@sha256:72b8f8d8a63c0664a729ec7afbfaa3e9ddb41d299253ed110b7358a91f55d144_ppc64le as a component of Red Hat OpenShift AI 3.4","status":"known_not_affected","remediations":[],"justification":"vulnerable_code_not_present","purl":"pkg:oci/odh-mod-arch-autorag-rhel9@sha256%3A72b8f8d8a63c0664a729ec7afbfaa3e9ddb41d299253ed110b7358a91f55d144?arch=ppc64le&repository_url=registry.redhat.io/rhoai/odh-mod-arch-autorag-rhel9&tag=1791860994","origins":[{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json"}]},{"source":"redhat","sourceId":"CVE-2026-100650","modified":"2026-10-19T11:32:22+00:00","url":"https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-100650.json","productId":"Red Hat OpenShift AI 3.4:registry.redhat.io/rhoai/odh-model-performance-data-rhel9@sha256:4ce3053cb04fdcdb6e2dfc5ba8f51cbd78d0d32c83f9e66ce376d273a592080f_ppc64le","product":"registry.redhat.io/rhoai/odh-model-performance-data-rhel9@sha256:4ce3053cb04fdcdb6e2dfc5ba8f51cbd78d0d32c83f9e66ce376d273a592080f_ppc64le as a component of Red Hat OpenShift AI 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