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      {
        "category": "summary",
        "text": "Red Hat AI Inference Server 3.3.6 (ROCm) is now available.",
        "title": "Topic"
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        "category": "general",
        "text": "Red Hat\u00ae AI Inference Server",
        "title": "Details"
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        "text": "This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.",
        "title": "Terms of Use"
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    "title": "Red Hat Security Advisory: Red Hat AI Inference Server 3.3.6 (ROCm)",
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        "name": "Reachable Assertion"
      },
      "discovery_date": "2026-06-22T23:01:00.799590+00:00",
      "ids": [
        {
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          "text": "2491582"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
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        ]
      },
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          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-41523"
        },
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          "summary": "RHBZ#2491582",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491582"
        },
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-41523",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-41523"
        },
        {
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          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3",
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        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r"
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      "release_date": "2026-06-22T22:18:14.494000+00:00",
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          "date": "2026-08-24T16:51:58+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:59139",
          "product_ids": [
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          ],
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          },
          "url": "https://access.redhat.com/errata/RHSA-2026:59139"
        },
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          "details": "Avoid running vLLM with python -O or PYTHONOPTIMIZE=1 until updated packages are available. Only load models from trusted sources. Restrict who can deploy or update models on inference endpoints. Apply network access controls and authentication in front of vLLM APIs.",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ]
        }
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            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
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          ]
        }
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          "details": "Important"
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      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-47155",
      "cwe": {
        "id": "CWE-829",
        "name": "Inclusion of Functionality from Untrusted Control Sphere"
      },
      "discovery_date": "2026-06-22T23:00:54.705583+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491580"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The revision pinning controls in vLLM do not consistently apply to all artifacts loaded for a model. This allows a deployment configured with specific revisions to still load dynamic code or other configuration files from an unpinned or default revision. This issue can lead to a supply-chain integrity compromise, where operators may unknowingly serve models with unreviewed or unintended behavior.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Moderate impact. The flaw is a supply-chain integrity issue when operators pin a HuggingFace model revision but vLLM may still load nested artifacts from an unpinned revision. It affects Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI images that ship vLLM versions prior to 0.22.0. KServe control-plane components that bundle vLLM as a library are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-47155"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491580",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491580"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-47155",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-47155"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-47155"
        },
        {
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          "summary": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6",
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          "summary": "https://github.com/vllm-project/vllm/pull/42616",
          "url": "https://github.com/vllm-project/vllm/pull/42616"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3ww4-5jv9-j5gm"
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          "url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
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      ],
      "release_date": "2026-06-22T22:20:10.793000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-24T16:51:58+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:59139",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:59139"
        },
        {
          "category": "workaround",
          "details": "Upgrade to a vLLM build containing the fix (\u003e= 0.22.0) when available from Red Hat. Until then, only serve models from trusted registries, pin revisions explicitly, and review nested artifacts in model repositories before deployment.",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ]
        }
      ],
      "scores": [
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            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "NONE",
            "baseScore": 6.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "LOW",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N",
            "version": "3.1"
          },
          "products": [
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          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Moderate"
        }
      ],
      "title": "vllm: vLLM: Supply-chain integrity issue due to inconsistent revision pinning controls"
    },
    {
      "cve": "CVE-2026-53923",
      "cwe": {
        "id": "CWE-824",
        "name": "Access of Uninitialized Pointer"
      },
      "discovery_date": "2026-06-22T23:00:51.742636+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491579"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM\u0027s GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other users\u0027 requests. This constitutes an information disclosure vulnerability.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Information disclosure via integer truncation",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Low impact for Red Hat AI products. The upstream issue is limited information disclosure via integer truncation in vLLM sampling parameters. Red Hat OpenShift AI, Red Hat AI Inference Server, and Red Hat Enterprise Linux AI images are not considered affected because untrusted clients cannot control the vulnerable parameters in supported deployment models.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-53923"
        },
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          "summary": "RHBZ#2491579",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491579"
        },
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-53923",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-53923"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53923"
        },
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          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e",
          "url": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e"
        },
        {
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          "summary": "https://github.com/vllm-project/vllm/pull/44971",
          "url": "https://github.com/vllm-project/vllm/pull/44971"
        },
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          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-5jv2-g5wq-cmr4"
        }
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          "date": "2026-08-24T16:51:58+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:59139",
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      ],
      "title": "vLLM: vLLM: Denial of Service due to excessive video frame processing"
    },
    {
      "cve": "CVE-2026-34756",
      "cwe": {
        "id": "CWE-1284",
        "name": "Improper Validation of Specified Quantity in Input"
      },
      "discovery_date": "2026-04-06T16:03:45.222577+00:00",
      "ids": [
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          "system_name": "Red Hat Bugzilla ID",
          "text": "2455425"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit this vulnerability by sending a specially crafted HTTP request with an excessively large \u0027n\u0027 parameter to the vLLM OpenAI-compatible API server. This can lead to a Denial of Service (DoS) by consuming excessive memory and blocking the system\u0027s event loop, causing the server to crash.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via excessively large \u0027n\u0027 parameter in OpenAI-compatible API",
          "title": "Vulnerability summary"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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        {
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          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34756"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380",
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        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/37952",
          "url": "https://github.com/vllm-project/vllm/pull/37952"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528"
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      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-24T17:15:02+00:00",
          "details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.4",
          "product_ids": [
            "Red Hat Enterprise Linux AI 3.4:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64"
          ],
          "restart_required": {
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          "product_ids": [
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          ]
        }
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          "cvss_v3": {
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            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
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    },
    {
      "cve": "CVE-2026-41523",
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      "discovery_date": "2026-06-22T23:01:00.799590+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491582"
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        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.",
          "title": "Vulnerability description"
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          "text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
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          "category": "external",
          "summary": "RHBZ#2491582",
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        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-41523",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-41523"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523"
        },
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          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3",
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          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r",
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        },
        {
          "category": "external",
          "summary": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c",
          "url": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c"
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        {
          "category": "vendor_fix",
          "date": "2026-08-24T17:15:02+00:00",
          "details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.4",
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            "Red Hat Enterprise Linux AI 3.4:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:59144"
        },
        {
          "category": "workaround",
          "details": "Avoid running vLLM with python -O or PYTHONOPTIMIZE=1 until updated packages are available. Only load models from trusted sources. Restrict who can deploy or update models on inference endpoints. Apply network access controls and authentication in front of vLLM APIs.",
          "product_ids": [
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            "baseScore": 7.5,
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            "privilegesRequired": "NONE",
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            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
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    },
    {
      "cve": "CVE-2026-44223",
      "cwe": {
        "id": "CWE-130",
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      "discovery_date": "2026-05-12T21:02:06.713372+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
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        }
      ],
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        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The extract_hidden_states speculative decoding proposer returns a tensor with an incorrect shape after the first decode step. This can be triggered by a remote attacker sending a request that uses sampling penalty parameters, such as repetition_penalty, frequency_penalty, or presence_penalty. Successful exploitation leads to a RuntimeError that crashes the EngineCore process, resulting in a denial of service (DoS).",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via malformed tensor shape in speculative decoding",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Important denial of service flaw in vLLM, as used in Red Hat AI Inference Server and Red Hat OpenShift AI, allows a remote attacker to crash the EngineCore process. By sending a request with specific sampling penalty parameters, an attacker can trigger an incorrect tensor shape, leading to a service disruption for affected AI inference workloads.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat Enterprise Linux AI 3.4:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64"
        ]
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      "references": [
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          "category": "self",
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          "url": "https://access.redhat.com/security/cve/CVE-2026-44223"
        },
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          "category": "external",
          "summary": "RHBZ#2476827",
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-44223",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-44223"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/38610",
          "url": "https://github.com/vllm-project/vllm/pull/38610"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw",
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          "details": "The container disk images provided by this update can be downloaded from the Red Hat container registry at registry.redhat.io using the \"podman pull\" command, for use with OpenShift Virtualization.\n\nFor details on deploying and configuring RHEL AI, see the Red Hat Enterprise Linux AI documentation at https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/3.4",
          "product_ids": [
            "Red Hat Enterprise Linux AI 3.4:registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64"
          ],
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          "url": "https://access.redhat.com/errata/RHSA-2026:59144"
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            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
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    "distribution": {
      "text": "Copyright \u00a9 Red Hat, Inc. All rights reserved.",
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        "url": "https://www.first.org/tlp/"
      }
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    "lang": "en",
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        "category": "summary",
        "text": "Updated Red Hat Enterprise Linux AI 3.4.4 container images are now available.",
        "title": "Topic"
      },
      {
        "category": "general",
        "text": "Red Hat\u00ae Enterprise Linux\u00ae AI is a foundation model platform to seamlessly develop, test, and run Granite family large language models (LLMs) for enterprise applications.\n\nThis update provides the latest Red Hat Enterprise Linux AI 3.4.4 container images.\n\nNote: Intel Gaudi accelerator support is included as a Technology Preview feature in this release. Technology Preview features are not fully supported under Red Hat Subscription Level Agreements, may not be functionally complete, and are not intended for production use.\n\nFor a full list of changes in this release, see the Red Hat Enterprise Linux AI Release Notes linked in the References section.",
        "title": "Details"
      },
      {
        "category": "legal_disclaimer",
        "text": "This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.",
        "title": "Terms of Use"
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            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 6.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
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          "category": "impact",
          "details": "Important"
        }
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      "title": "vLLM: vLLM: Denial of Service due to excessive video frame processing"
    },
    {
      "cve": "CVE-2026-34756",
      "cwe": {
        "id": "CWE-1284",
        "name": "Improper Validation of Specified Quantity in Input"
      },
      "discovery_date": "2026-04-06T16:03:45.222577+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2455425"
        }
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        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit this vulnerability by sending a specially crafted HTTP request with an excessively large \u0027n\u0027 parameter to the vLLM OpenAI-compatible API server. This can lead to a Denial of Service (DoS) by consuming excessive memory and blocking the system\u0027s event loop, causing the server to crash.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via excessively large \u0027n\u0027 parameter in OpenAI-compatible API",
          "title": "Vulnerability summary"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
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          "summary": "Canonical URL",
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          "category": "external",
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          "url": "https://www.cve.org/CVERecord?id=CVE-2026-34756"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-34756",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34756"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380",
          "url": "https://github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380"
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          "category": "external",
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          "url": "https://github.com/vllm-project/vllm/pull/37952"
        },
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          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528"
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            "Red Hat Enterprise Linux AI 3.4:registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:38ce057f97034bbb8eaea574d41cc7eebaeaa9265a4cdd966476dd3fac129c3c_amd64"
          ],
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            "category": "none"
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          "url": "https://access.redhat.com/errata/RHSA-2026:59151"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
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            "attackVector": "NETWORK",
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            "confidentialityImpact": "NONE",
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            "scope": "UNCHANGED",
            "userInteraction": "NONE",
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          "details": "Important"
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      "title": "vllm: vLLM: Denial of Service via excessively large \u0027n\u0027 parameter in OpenAI-compatible API"
    },
    {
      "cve": "CVE-2026-41523",
      "cwe": {
        "id": "CWE-617",
        "name": "Reachable Assertion"
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      "discovery_date": "2026-06-22T23:01:00.799590+00:00",
      "ids": [
        {
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        {
          "category": "description",
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          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
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          "date": "2026-08-24T17:35:47+00:00",
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          "title": "Vulnerability description"
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          "title": "Statement"
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          "date": "2026-08-31T13:38:30+00:00",
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            "userInteraction": "NONE",
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      "title": "urllib3: urllib3: Unbounded decompression chain leads to resource exhaustion"
    },
    {
      "cve": "CVE-2026-0994",
      "cwe": {
        "id": "CWE-674",
        "name": "Uncontrolled Recursion"
      },
      "discovery_date": "2026-01-23T16:02:59.235878+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2432398"
        }
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        {
          "category": "description",
          "text": "A flaw was found in protobuf. A remote attacker can exploit this denial-of-service (DoS) vulnerability by supplying deeply nested `google.protobuf.Any` messages to the `google.protobuf.json_format.ParseDict()` function. This bypasses the intended recursion depth limit, leading to the exhaustion of Python\u2019s recursion stack and causing a `RecursionError`, which results in a denial of service.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "python: protobuf: Protobuf: Denial of Service due to recursion depth bypass",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability is rated Important for Red Hat products. The flaw in `protobuf` allows a remote attacker to trigger a denial-of-service by providing specially crafted, deeply nested `google.protobuf.Any` messages to the `google.protobuf.json_format.ParseDict()` function. This bypasses the intended recursion depth limit, leading to resource exhaustion and application crashes.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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          "category": "self",
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        {
          "category": "external",
          "summary": "https://github.com/protocolbuffers/protobuf/pull/25239",
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      "release_date": "2026-01-23T14:55:16.876000+00:00",
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    {
      "cve": "CVE-2026-11822",
      "cwe": {
        "id": "CWE-125",
        "name": "Out-of-bounds Read"
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          "text": "2487258"
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          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "sqlite: SQLite: Arbitrary code execution via crafted FTS5 full-text search data",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Important: This flaw in the SQLite FTS5 full-text search extension can lead to arbitrary code execution or denial of service. Exploitation requires an application to process a specially crafted SQLite database containing malformed FTS5 page data, which could occur if an attacker provides a malicious database to a vulnerable application. This risk is elevated in scenarios where applications handle untrusted SQLite database files.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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          "summary": "Canonical URL",
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          "summary": "RHBZ#2487258",
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-11822",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-11822"
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          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-11822",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-11822"
        },
        {
          "category": "external",
          "summary": "https://sqlite.org/releaselog/3_53_2.html",
          "url": "https://sqlite.org/releaselog/3_53_2.html"
        },
        {
          "category": "external",
          "summary": "https://sqlite.org/src/info/061febcf41ca",
          "url": "https://sqlite.org/src/info/061febcf41ca"
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          "url": "https://sqlite.org/src/info/4a5ad516ea93"
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          "summary": "https://www.vulncheck.com/advisories/sqlite-before-memory-corruption-in-fts5-extension",
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      "title": "sqlite: SQLite: Arbitrary code execution via crafted FTS5 full-text search data"
    },
    {
      "cve": "CVE-2026-11824",
      "cwe": {
        "id": "CWE-191",
        "name": "Integer Underflow (Wrap or Wraparound)"
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      "discovery_date": "2026-06-09T20:02:19.874232+00:00",
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          "title": "Vulnerability description"
        },
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          "text": "sqlite: SQLite: Arbitrary code execution and crash via heap-based buffer overflow in FTS5",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Important vulnerability in SQLite\u0027s FTS5 full-text search extension can lead to arbitrary code execution or application crashes due to a heap-based buffer overflow. Exploitation requires an attacker to provide a specially crafted SQLite database, which must then be processed by an application compiled with FTS5 support. While requiring user interaction and local access, the potential for full impact on system resources justifies the Important severity.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
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          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-11824"
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          "summary": "https://sqlite.org/releaselog/3_53_2.html",
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            "privilegesRequired": "NONE",
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          "details": "Important"
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      "title": "sqlite: SQLite: Arbitrary code execution and crash via heap-based buffer overflow in FTS5"
    },
    {
      "cve": "CVE-2026-40192",
      "cwe": {
        "id": "CWE-409",
        "name": "Improper Handling of Highly Compressed Data (Data Amplification)"
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      "discovery_date": "2026-04-16T00:00:49.590876+00:00",
      "ids": [
        {
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          "text": "2458856"
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      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to trigger a denial of service (DoS) by providing a specially crafted FITS image file. The library\u0027s failure to limit the amount of GZIP-compressed data during decoding can lead to unbounded memory consumption, causing the system to crash or experience severe performance issues.",
          "title": "Vulnerability description"
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          "title": "Vulnerability summary"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-40192"
        },
        {
          "category": "external",
          "summary": "RHBZ#2458856",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2458856"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-40192",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-40192"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-40192",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-40192"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/3cb854e8b2bab43f40e342e665f9340d861aa628",
          "url": "https://github.com/python-pillow/Pillow/commit/3cb854e8b2bab43f40e342e665f9340d861aa628"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/pull/9521",
          "url": "https://github.com/python-pillow/Pillow/pull/9521"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-whj4-6x5x-4v2j",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-whj4-6x5x-4v2j"
        },
        {
          "category": "external",
          "summary": "https://pillow.readthedocs.io/en/stable/releasenotes/12.2.0.html#prevent-fits-decompression-bomb",
          "url": "https://pillow.readthedocs.io/en/stable/releasenotes/12.2.0.html#prevent-fits-decompression-bomb"
        }
      ],
      "release_date": "2026-04-15T22:53:56.147000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "Pillow: Pillow: Denial of Service via decompression bomb in FITS image processing"
    },
    {
      "cve": "CVE-2026-41523",
      "cwe": {
        "id": "CWE-617",
        "name": "Reachable Assertion"
      },
      "discovery_date": "2026-06-22T23:01:00.799590+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491582"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). An unauthenticated attacker can exploit an assert-based security check during activation function loading. By publishing a malicious HuggingFace model, an attacker can achieve arbitrary code execution on the server when vLLM runs in Python optimized mode.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Red Hat rates this issue as having Important impact for Red Hat AI Inference Server and Red Hat OpenShift AI vLLM serving images, and Moderate impact for Red Hat Enterprise Linux AI bootc images that bundle vLLM. Exploitation requires loading an untrusted HuggingFace cross-encoder model while the vLLM process runs with Python optimized mode (python -O or PYTHONOPTIMIZE=1). Red Hat AI Inference Server 3.2/3.3 images and other components without the vulnerable pooler activation loader (vLLM \u003c 0.14.0) are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-41523"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491582",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491582"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-41523",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-41523"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-41523"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3",
          "url": "https://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3r"
        },
        {
          "category": "external",
          "summary": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c",
          "url": "https://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64c"
        }
      ],
      "release_date": "2026-06-22T22:18:14.494000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Avoid running vLLM with python -O or PYTHONOPTIMIZE=1 until updated packages are available. Only load models from trusted sources. Restrict who can deploy or update models on inference endpoints. Apply network access controls and authentication in front of vLLM APIs.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-44222",
      "cwe": {
        "id": "CWE-1285",
        "name": "Improper Validation of Specified Index, Position, or Offset in Input"
      },
      "discovery_date": "2026-05-12T21:02:52.278811+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2476840"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). This vulnerability allows unauthenticated attackers to cause a Denial of Service (DoS) by supplying image or video placeholder sequences without matching data, leading to an unhandled error and worker termination. Additionally, text-only prompts containing special tokens can be misinterpreted as control commands, potentially leading to unexpected behavior.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via malformed multimodal input or token injection",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This is an Important denial of service vulnerability affecting vLLM, as integrated into Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI. The flaw allows unauthenticated attackers to trigger worker termination and degrade service availability by submitting malformed multimodal input or text prompts containing specific token sequences. This impact is significant due to the potential for service disruption in AI inference workloads.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-44222"
        },
        {
          "category": "external",
          "summary": "RHBZ#2476840",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2476840"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-44222",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-44222"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-44222",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44222"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/issues/32656",
          "url": "https://github.com/vllm-project/vllm/issues/32656"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f"
        }
      ],
      "release_date": "2026-05-12T19:57:25.336000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed multimodal input or token injection"
    },
    {
      "cve": "CVE-2026-48586",
      "cwe": {
        "id": "CWE-409",
        "name": "Improper Handling of Highly Compressed Data (Data Amplification)"
      },
      "discovery_date": "2026-07-27T12:01:11.736259+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2507434"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Apache Thrift. This vulnerability, categorized as improper handling of highly compressed data (also known as data amplification), allows a remote attacker to cause a Denial of Service (DoS) by sending specially crafted, highly compressed data. The affected component fails to properly manage the expansion of this data, leading to resource exhaustion and system unavailability.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "thrift: org.apache.thrift/libthrift: github.com/apache/thrift: Apache Thrift: Denial of Service via improper handling of highly compressed data",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Important vulnerability in Apache Thrift\u0027s handling of highly compressed data can lead to a denial of service. Remote, unauthenticated attackers could exploit this flaw by sending specially crafted compressed data, causing excessive resource consumption and disrupting the availability of services utilizing vulnerable Thrift bindings in Red Hat products.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-48586"
        },
        {
          "category": "external",
          "summary": "RHBZ#2507434",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2507434"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-48586",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-48586"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-48586",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-48586"
        },
        {
          "category": "external",
          "summary": "http://www.openwall.com/lists/oss-security/2026/07/24/37",
          "url": "http://www.openwall.com/lists/oss-security/2026/07/24/37"
        },
        {
          "category": "external",
          "summary": "https://lists.apache.org/thread/7v3jhgwfbmhx42424phydlnzb109g8b9",
          "url": "https://lists.apache.org/thread/7v3jhgwfbmhx42424phydlnzb109g8b9"
        },
        {
          "category": "external",
          "summary": "https://lists.apache.org/thread/p008svsjf9p6bj47wyyf5dgglq5z7xoq",
          "url": "https://lists.apache.org/thread/p008svsjf9p6bj47wyyf5dgglq5z7xoq"
        }
      ],
      "release_date": "2026-07-27T11:02:54.430000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "thrift: org.apache.thrift/libthrift: github.com/apache/thrift: Apache Thrift: Denial of Service via improper handling of highly compressed data"
    },
    {
      "cve": "CVE-2026-48746",
      "cwe": {
        "id": "CWE-501",
        "name": "Trust Boundary Violation"
      },
      "discovery_date": "2026-06-22T23:00:57.824402+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491581"
        }
      ],
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        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). This vulnerability, residing in ASGI web servers and Starlette\u0027s trust in them, allows an attacker to bypass the OpenAI API Authentication Middleware. This bypass enables unauthorized access to the API without requiring the configured VLLM_API_KEY or --api-key, leading to critical unauthorized operations.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: starlette: vLLM: Critical authentication bypass allows unauthorized API access",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "CVE-2026-48746 is an authentication bypass in the vLLM OpenAI-compatible API server. A remote attacker who can reach the vLLM endpoint directly can craft a Host header so the authentication middleware checks a different URL path than the one actually dispatched, bypassing VLLM_API_KEY / --api-key protection. Successful exploitation allows unauthorized inference API access, which can result in confidentiality loss (model/prompt abuse) and availability impact (resource exhaustion). The flaw does not provide integrity compromise or arbitrary code execution. Exploitation requires vLLM API-key authentication to be enabled and the service to be exposed without an RFC-conforming reverse proxy that normalizes the Host header. Because Red Hat AI inference offerings are commonly deployed behind OpenShift Routes or similar proxies, and because the vulnerability is conditional on deployment and configuration, the overall flaw impact is rated Important rather than Critical.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-48746"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491581",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491581"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-48746",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-48746"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-48746",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-48746"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/43426",
          "url": "https://github.com/vllm-project/vllm/pull/43426"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-94f4-hr76-p5j6",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-94f4-hr76-p5j6"
        },
        {
          "category": "external",
          "summary": "https://x41-dsec.de/lab/advisories/x41-2026-002-starlette",
          "url": "https://x41-dsec.de/lab/advisories/x41-2026-002-starlette"
        }
      ],
      "release_date": "2026-06-22T21:57:28.997000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Restrict network access to the vLLM API endpoint to only trusted clients and internal networks. Implement firewall rules or network policies to limit inbound connections to the vLLM service, thereby reducing the attack surface. This operational control helps prevent unauthorized external access to the vulnerable API.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
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      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 9.1,
            "baseSeverity": "CRITICAL",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:H",
            "version": "3.1"
          },
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: starlette: vLLM: Critical authentication bypass allows unauthorized API access"
    },
    {
      "cve": "CVE-2026-50193",
      "cwe": {
        "id": "CWE-1050",
        "name": "Excessive Platform Resource Consumption within a Loop"
      },
      "discovery_date": "2026-06-23T22:01:01.154533+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491999"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in jackson-databind, a general-purpose data-binding library for Jackson Data Processor. A remote attacker can exploit this vulnerability by sending deeply nested JSON (JavaScript Object Notation) data to a service that reads and processes it. This can lead to a Denial of Service (DoS) by consuming significant system resources, making the service unavailable to legitimate users.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "jackson-databind: Jackson-databind: Denial of Service via deeply nested JSON processing",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This is an Important denial-of-service flaw in jackson-databind, affecting services that process deeply nested JSON data. Exploitation occurs when a service reads JSON as `JsonNode` and subsequently writes it back using `JsonNode.toString()`, leading to excessive resource consumption and potential service unavailability.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-50193"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491999",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491999"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-50193",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-50193"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-50193",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-50193"
        },
        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/commit/a1fa4ae4ecf5cee16da465985f135f3e81816f8c",
          "url": "https://github.com/FasterXML/jackson-databind/commit/a1fa4ae4ecf5cee16da465985f135f3e81816f8c"
        },
        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/issues/3447",
          "url": "https://github.com/FasterXML/jackson-databind/issues/3447"
        },
        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/security/advisories/GHSA-3wrr-7qpf-2prh",
          "url": "https://github.com/FasterXML/jackson-databind/security/advisories/GHSA-3wrr-7qpf-2prh"
        }
      ],
      "release_date": "2026-06-23T21:00:20.444000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
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          ],
          "restart_required": {
            "category": "none"
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          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
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      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "jackson-databind: Jackson-databind: Denial of Service via deeply nested JSON processing"
    },
    {
      "cve": "CVE-2026-54060",
      "cwe": {
        "id": "CWE-1050",
        "name": "Excessive Platform Resource Consumption within a Loop"
      },
      "discovery_date": "2026-07-06T20:02:29.509113+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2497466"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. When processing a specially crafted font file, the library\u0027s font compilation function does not adequately check for excessive memory allocation. This oversight allows a remote attacker to trigger an unreasonable consumption of system memory, leading to a denial of service (DoS) for the application.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "python-pillow: Pillow: Denial of Service via excessive memory allocation when processing font files",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "A flaw was found in Pillow prior to version 12.3.0. The FontFile.compile() method in PIL/FontFile.py assembles per-glyph images into a combined bitmap using Image.new() without calling Image._decompression_bomb_check(). This allows a specially crafted font file to trigger excessive memory allocation during conversion or saving, leading to denial of service via resource exhaustion.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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      },
      "references": [
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          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54060"
        },
        {
          "category": "external",
          "summary": "RHBZ#2497466",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497466"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54060",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54060"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54060",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54060"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst",
          "url": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/0a263e6264aa5399988d9acd3bbfbca2ca3ec77d",
          "url": "https://github.com/python-pillow/Pillow/commit/0a263e6264aa5399988d9acd3bbfbca2ca3ec77d"
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        {
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          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-5x94-69rx-g8h2",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-5x94-69rx-g8h2"
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      "release_date": "2026-07-06T18:49:23.788000+00:00",
      "remediations": [
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          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
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      "scores": [
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          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
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            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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      "threats": [
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          "details": "Important"
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      "title": "python-pillow: Pillow: Denial of Service via excessive memory allocation when processing font files"
    },
    {
      "cve": "CVE-2026-54234",
      "cwe": {
        "id": "CWE-125",
        "name": "Out-of-bounds Read"
      },
      "discovery_date": "2026-07-06T21:01:59.789808+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2497515"
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      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). A remote attacker can exploit this vulnerability by sending a specially crafted multi-request speculative decoding workload through public gRPC Generate and Abort endpoints. This malformed workload can cause the rejection sampler to produce an out-of-vocabulary token, which then crashes the engine worker. This leads to a service-wide Denial of Service (DoS) for all clients until the worker is restarted.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via malformed speculative decoding workload",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Important denial of service flaw in vLLM, utilized by Red Hat AI Inference Server, Red Hat Enterprise Linux AI, and Red Hat OpenShift AI, allows a remote attacker to crash the engine worker. By sending a specially crafted multi-request speculative decoding workload to public gRPC endpoints, an attacker can trigger an out-of-vocabulary token, leading to a service-wide disruption for all connected clients.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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          "summary": "Canonical URL",
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          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54234",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54234"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54234",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54234"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca",
          "url": "https://github.com/vllm-project/vllm/commit/8a5cf1ccd65e8ac7635c402c1ec0b08988bc26ca"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/44744",
          "url": "https://github.com/vllm-project/vllm/pull/44744"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-8wr5-jm2h-8r4f"
        }
      ],
      "release_date": "2026-07-06T19:49:20.481000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "To mitigate this issue, restrict network access to the vLLM inference engine\u0027s gRPC Generate and Abort endpoints. Configure firewall rules to limit incoming connections to trusted clients or internal networks only. This will prevent remote, unauthenticated attackers from sending malformed workloads and triggering a denial of service. If the service is exposed via a proxy or load balancer, ensure that access controls are in place at that layer.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed speculative decoding workload"
    },
    {
      "cve": "CVE-2026-54235",
      "cwe": {
        "id": "CWE-1287",
        "name": "Improper Validation of Specified Type of Input"
      },
      "discovery_date": "2026-06-22T23:01:07.102249+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491584"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). The temperature validation gates, which use comparison operators, incorrectly handle Not-a-Number (NaN) and positive Infinity values in Python\u0027s IEEE 754 float semantics. These invalid values can bypass validation and propagate to GPU sampling kernels, leading to undefined behavior or CUDA errors that can crash the inference worker. This could allow an attacker to cause a Denial of Service (DoS) by providing specially crafted input.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service due to improper floating-point validation",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Moderate impact flaw in vLLM, as used in Red Hat AI Inference Server, Red Hat OpenShift AI, and Red Hat Enterprise Linux AI, allows for a denial of service. Improper validation of floating-point values like Not-a-Number (NaN) or positive Infinity in temperature parameters can bypass security checks, leading to undefined behavior or CUDA errors that crash the inference worker. This could be exploited by providing specially crafted input to the LLM inference engine.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54235"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491584",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491584"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54235",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54235"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54235",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54235"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/d598d239737cfa37bcfcb98886ec3f3557fc7198",
          "url": "https://github.com/vllm-project/vllm/commit/d598d239737cfa37bcfcb98886ec3f3557fc7198"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/45116",
          "url": "https://github.com/vllm-project/vllm/pull/45116"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-7h4p-rffg-7823",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-7h4p-rffg-7823"
        }
      ],
      "release_date": "2026-06-22T21:59:02.710000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "LOW",
            "baseScore": 6.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Moderate"
        }
      ],
      "title": "vllm: vLLM: Denial of Service due to improper floating-point validation"
    },
    {
      "cve": "CVE-2026-54283",
      "cwe": {
        "id": "CWE-770",
        "name": "Allocation of Resources Without Limits or Throttling"
      },
      "discovery_date": "2026-06-22T18:01:06.194658+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491440"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Starlette where the request.form() method silently ignores configured resource limits (max_fields and max_part_size) when parsing application/x-www-form-urlencoded data. An unauthenticated attacker can exploit this by sending a urlencoded request body with an arbitrarily large number of fields or an oversized field, causing denial of service through resource exhaustion.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "starlette: Starlette: request.form() limits silently ignored for application/x-www-form-urlencoded enable DoS",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "A flaw was found in Starlette where the request.form() method silently ignores configured resource limits (max_fields and max_part_size) when parsing application/x-www-form-urlencoded data. An unauthenticated attacker can exploit this by sending a urlencoded request body with an arbitrarily large number of fields or an oversized field, causing denial of service through resource exhaustion. This only affects applications that explicitly call request.form() on urlencoded input; JSON-only APIs and services where Starlette is a transitive dependency not used for form parsing are not affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54283"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491440",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491440"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54283",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54283"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54283",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54283"
        },
        {
          "category": "external",
          "summary": "https://github.com/Kludex/starlette/security/advisories/GHSA-82w8-qh3p-5jfq",
          "url": "https://github.com/Kludex/starlette/security/advisories/GHSA-82w8-qh3p-5jfq"
        }
      ],
      "release_date": "2026-06-22T16:46:16.706000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "starlette: Starlette: request.form() limits silently ignored for application/x-www-form-urlencoded enable DoS"
    },
    {
      "cve": "CVE-2026-54399",
      "cwe": {
        "id": "CWE-770",
        "name": "Allocation of Resources Without Limits or Throttling"
      },
      "discovery_date": "2026-07-01T18:02:08.214968+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2496101"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Apache HttpComponents Core. This uncontrolled resource consumption vulnerability in the HTTP/1.1 message parser allows a remote attacker to cause a denial of service through memory exhaustion. This can be triggered by sending messages with an excessive number of headers or excessive header length.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "org.apache.httpcomponents.core5/httpcore5: Apache HttpComponents Core: Denial of Service via excessive HTTP headers",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Important: This flaw in Apache HttpComponents Core allows a remote attacker to cause a denial of service by sending HTTP/1.1 messages with an excessive number or length of headers. This can lead to memory exhaustion in applications using the affected HTTP/1.1 message parser, impacting service availability.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54399"
        },
        {
          "category": "external",
          "summary": "RHBZ#2496101",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2496101"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54399",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54399"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54399",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54399"
        },
        {
          "category": "external",
          "summary": "http://www.openwall.com/lists/oss-security/2026/07/01/4",
          "url": "http://www.openwall.com/lists/oss-security/2026/07/01/4"
        },
        {
          "category": "external",
          "summary": "https://lists.apache.org/thread/zmxh1pl2zohov5ntdh4lt85gfrlchgpy",
          "url": "https://lists.apache.org/thread/zmxh1pl2zohov5ntdh4lt85gfrlchgpy"
        }
      ],
      "release_date": "2026-07-01T17:02:21.778000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
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          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "To mitigate this issue, configure an upstream proxy or web application firewall (WAF) to enforce limits on the number and length of HTTP headers. This prevents malformed requests from reaching and exhausting the vulnerable Apache HttpComponents Core component. Consult your proxy or WAF documentation for specific configuration. A service restart may be required for changes to take effect.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
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        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "org.apache.httpcomponents.core5/httpcore5: Apache HttpComponents Core: Denial of Service via excessive HTTP headers"
    },
    {
      "cve": "CVE-2026-54512",
      "cwe": {
        "id": "CWE-502",
        "name": "Deserialization of Untrusted Data"
      },
      "discovery_date": "2026-06-23T22:01:49.821346+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2492015"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in jackson-databind. This vulnerability allows a remote attacker to bypass the PolymorphicTypeValidator (PTV) when polymorphic typing is enabled and a type identifier contains generic parameters. By crafting a malicious type ID, an attacker can place a denied class as a generic type parameter of an allowed container. This leads to the loading and instantiation of arbitrary classes, potentially resulting in arbitrary code execution.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "jackson-databind: jackson-databind: Arbitrary code execution via PolymorphicTypeValidator bypass",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability in `jackson-databind` is rated as Important. While it describes a potential bypass of the PolymorphicTypeValidator leading to arbitrary code execution, exploitation requires an attacker to already possess administrative privileges, specifically the `manage-realm` role. This flaw does not enable privilege escalation, as it does not grant additional capabilities beyond those inherently available to an existing administrator.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54512"
        },
        {
          "category": "external",
          "summary": "RHBZ#2492015",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2492015"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-54512",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-54512"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-54512",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-54512"
        },
        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/commit/434d6c511de7fdd9872f29157aafb6162d12d8d5",
          "url": "https://github.com/FasterXML/jackson-databind/commit/434d6c511de7fdd9872f29157aafb6162d12d8d5"
        },
        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/issues/5988",
          "url": "https://github.com/FasterXML/jackson-databind/issues/5988"
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        {
          "category": "external",
          "summary": "https://github.com/FasterXML/jackson-databind/security/advisories/GHSA-j3rv-43j4-c7qm",
          "url": "https://github.com/FasterXML/jackson-databind/security/advisories/GHSA-j3rv-43j4-c7qm"
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      ],
      "release_date": "2026-06-23T20:56:36.646000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
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          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
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      "scores": [
        {
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            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "jackson-databind: jackson-databind: Arbitrary code execution via PolymorphicTypeValidator bypass"
    },
    {
      "cve": "CVE-2026-55379",
      "cwe": {
        "id": "CWE-770",
        "name": "Allocation of Resources Without Limits or Throttling"
      },
      "discovery_date": "2026-07-06T20:01:29.922515+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2497452"
        }
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      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows a remote attacker to cause a Denial of Service (DoS) by providing a specially crafted BDF font file. The library\u0027s image processing function fails to properly validate dimensions from the font file, bypassing a critical security check designed to prevent excessive memory usage. This oversight can lead to the system consuming an unreasonable amount of memory, making it unavailable to legitimate users.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "python-pillow: Pillow: Denial of Service via crafted BDF font file",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "A flaw was found in the Pillow Python imaging library. The BDF font file parser passes attacker-controlled dimensions to Image.new() without decompression bomb validation, allowing excessive memory allocation and denial of service.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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        ]
      },
      "references": [
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          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-55379"
        },
        {
          "category": "external",
          "summary": "RHBZ#2497452",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497452"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-55379",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-55379"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55379",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55379"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst",
          "url": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/0a263e6264aa5399988d9acd3bbfbca2ca3ec77d",
          "url": "https://github.com/python-pillow/Pillow/commit/0a263e6264aa5399988d9acd3bbfbca2ca3ec77d"
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        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-45hq-cxwh-f6vc",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-45hq-cxwh-f6vc"
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      "release_date": "2026-07-06T18:52:11.633000+00:00",
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          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
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          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Do not load BDF font files from untrusted sources. Applications that only process standard image formats (PNG, JPEG, etc.) and do not use BdfFontFile or ImageFont.load() with BDF files are not affected.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
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            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "python-pillow: Pillow: Denial of Service via crafted BDF font file"
    },
    {
      "cve": "CVE-2026-55380",
      "cwe": {
        "id": "CWE-1285",
        "name": "Improper Validation of Specified Index, Position, or Offset in Input"
      },
      "discovery_date": "2026-07-06T20:01:42.245051+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2497455"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted GD 2.x image file. The GdImageFile._open() function reads image dimensions without proper validation, leading to excessive memory allocation. This can result in a Denial of Service (DoS) due to C-heap exhaustion.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "python-pillow: Pillow: Denial of Service via crafted GD 2.x image file",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "A denial-of-service vulnerability was found in Pillow\u0027s GdImageFile plugin. The GdImageFile._open() function reads image dimensions from GD 2.x file headers and stores them without performing a decompression bomb check. A crafted .gd file of approximately 1 KB can trigger an unchecked 4.3 GB C-heap allocation, potentially crashing the process or exhausting memory on systems with insufficient resources. This issue is fixed in Pillow 12.3.0.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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          "summary": "Canonical URL",
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          "summary": "RHBZ#2497455",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497455"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-55380",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-55380"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55380",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55380"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst",
          "url": "https://github.com/python-pillow/Pillow/blob/main/docs/releasenotes/12.3.0.rst"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/f39b0ae6624eb2d7c5c5d651d9bb5fdbd96a8675",
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          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-phj9-mv4w-65pm",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-phj9-mv4w-65pm"
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      "release_date": "2026-07-06T18:50:14.789000+00:00",
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          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
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        },
        {
          "category": "workaround",
          "details": "Avoid processing untrusted GD 2.x image files with PIL.GdImageFile.open(). Use Image.open() instead, which includes decompression bomb protections for supported formats. If GdImageFile must be used, validate the image dimensions before calling load(). Restricting accepted image formats at the application boundary to only those explicitly needed can reduce exposure.",
          "product_ids": [
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            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
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          "details": "Important"
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      "title": "python-pillow: Pillow: Denial of Service via crafted GD 2.x image file"
    },
    {
      "cve": "CVE-2026-55574",
      "cwe": {
        "id": "CWE-1333",
        "name": "Inefficient Regular Expression Complexity"
      },
      "discovery_date": "2026-07-06T21:01:40.112117+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2497509"
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        {
          "category": "description",
          "text": "A flaw was found in vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). A remote attacker could exploit this vulnerability by providing a specially crafted regular expression to the structured_outputs.regex API parameter. This adversarial regex, containing nested quantifiers, can cause an exponential expansion of the state-space in the grammar compiler, leading to an inference worker hanging indefinitely. This results in a Denial of Service (DoS) for the affected system.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "An Important denial of service vulnerability exists in vLLM, as utilized within Red Hat AI Inference Server and Red Hat OpenShift AI. This flaw allows a remote, unauthenticated attacker to cause an inference worker to hang indefinitely by submitting a specially crafted regular expression to the structured outputs API. The absence of complexity analysis for nested quantifiers in the regex compiler leads to an exponential state-space expansion, resulting in prolonged service disruption.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
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      "references": [
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          "summary": "Canonical URL",
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          "summary": "RHBZ#2497509",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497509"
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-55574",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-55574"
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          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55574",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55574"
        },
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          "summary": "https://github.com/vllm-project/vllm/commit/2b3006076c5e9bc4cda9e03e3641388de3c5c286",
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          "summary": "https://github.com/vllm-project/vllm/pull/45118",
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          "category": "external",
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          "date": "2026-08-31T13:38:30+00:00",
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          "product_ids": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
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      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API"
    },
    {
      "cve": "CVE-2026-55969",
      "cwe": {
        "id": "CWE-190",
        "name": "Integer Overflow or Wraparound"
      },
      "discovery_date": "2026-07-27T12:01:08.265881+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2507433"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Apache Thrift\u0027s C++, c_glib, Go, netstd, Delphi, and Haxe bindings. This integer overflow or wraparound vulnerability allows a remote attacker to cause a denial of service (DoS) by sending specially crafted input. The flaw can lead to the affected service becoming unavailable.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "thrift: github.com/apache/thrift: Apache Thrift: Denial of Service via integer overflow or wraparound",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability in Apache Thrift bindings, rated as Important, allows a remote attacker to trigger a denial of service. The integer overflow or wraparound flaw can lead to affected services becoming unavailable, impacting the reliability of systems utilizing vulnerable Thrift components across Red Hat products such as OpenShift Container Platform and Red Hat Ceph Storage.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-55969"
        },
        {
          "category": "external",
          "summary": "RHBZ#2507433",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2507433"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-55969",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-55969"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-55969",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-55969"
        },
        {
          "category": "external",
          "summary": "http://www.openwall.com/lists/oss-security/2026/07/24/40",
          "url": "http://www.openwall.com/lists/oss-security/2026/07/24/40"
        },
        {
          "category": "external",
          "summary": "https://lists.apache.org/thread/7v3jhgwfbmhx42424phydlnzb109g8b9",
          "url": "https://lists.apache.org/thread/7v3jhgwfbmhx42424phydlnzb109g8b9"
        },
        {
          "category": "external",
          "summary": "https://lists.apache.org/thread/xmkgd107k795hyrg5kf97mny30sgl5bo",
          "url": "https://lists.apache.org/thread/xmkgd107k795hyrg5kf97mny30sgl5bo"
        }
      ],
      "release_date": "2026-07-27T11:07:53.785000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
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      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "thrift: github.com/apache/thrift: Apache Thrift: Denial of Service via integer overflow or wraparound"
    },
    {
      "acknowledgments": [
        {
          "names": [
            "The FuzzAnything Team"
          ],
          "organization": "FuzzAnything"
        }
      ],
      "cve": "CVE-2026-56208",
      "cwe": {
        "id": "CWE-122",
        "name": "Heap-based Buffer Overflow"
      },
      "discovery_date": "2026-06-19T00:00:00+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2490799"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A heap buffer overflow vulnerability was found in libaom, the reference AV1 codec implementation. A flaw in the AV1 encoder\u0027s Look-Ahead Processing (LAP) mode causes the first-pass stats ring buffer wrap-around guard to be bypassed when g_lag_in_frames is set to 1 or higher. This results in a 232-byte out-of-bounds write on every encoded frame after the second, corrupting adjacent heap objects. An attacker who can influence encoder configuration in a transcoding service or WebRTC session could exploit this to cause a denial of service (process crash) or potentially achieve code execution.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "libaom: libaom: heap buffer overflow in AV1 encoder first-pass stats buffer via LAP mode",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability is rated as Important severity because a heap buffer overflow with attacker-influenced data can cause reliable denial of service and potentially lead to code execution, though the attacker has only indirect control over the written values (encoder-computed statistics). In Red Hat products, libaom ships bundled within Firefox and Thunderbird as a statically-linked dependency used for AV1 decoding and WebRTC encoding. The vulnerable code path requires the encoder to be configured with g_lag_in_frames \u003e= 1 (Look-Ahead Processing mode). In Firefox\u0027s WebRTC implementation, the encoder configuration is controlled by the browser itself and not exposed to remote peers, which significantly limits the attack surface compared to standalone transcoding services. RHEL-AI 3.4 and Hummingbird 1 ship standalone libaom (aom) packages at versions within the affected range. Applications on those platforms that use the libaom encoder API with LAP mode and accept untrusted configuration input are vulnerable.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-56208"
        },
        {
          "category": "external",
          "summary": "RHBZ#2490799",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2490799"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-56208",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-56208"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-56208",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-56208"
        },
        {
          "category": "external",
          "summary": "https://aomedia.googlesource.com/aom/+/243f8ae84b",
          "url": "https://aomedia.googlesource.com/aom/+/243f8ae84b"
        },
        {
          "category": "external",
          "summary": "https://issues.chromium.org/issues/504317456",
          "url": "https://issues.chromium.org/issues/504317456"
        }
      ],
      "release_date": "2026-06-19T00:00:00+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "There is no complete mitigation for this vulnerability. The following measures can reduce risk:\n\n1. If using libaom as a standalone encoder library, avoid setting g_lag_in_frames to values \u003e= 1 when processing untrusted input, or validate all encoder configuration parameters before passing them to the libaom API.\n2. For Firefox and Thunderbird, ensure browsers are updated to versions that include the patched libaom (v3.14.0 or later).\n3. For standalone libaom deployments (RHEL-AI, Hummingbird), restrict access to the encoding service to trusted clients only.\n4. Apply network-level access controls to limit who can submit video for encoding.",
          "product_ids": [
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          ]
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      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.6,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "libaom: libaom: heap buffer overflow in AV1 encoder first-pass stats buffer via LAP mode"
    },
    {
      "acknowledgments": [
        {
          "names": [
            "The FuzzAnything Team"
          ],
          "organization": "FuzzAnything"
        }
      ],
      "cve": "CVE-2026-56209",
      "cwe": {
        "id": "CWE-787",
        "name": "Out-of-bounds Write"
      },
      "discovery_date": "2026-06-19T00:00:00+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2490800"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "An arbitrary address write vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows an attacker to inject an arbitrary pointer into the cyclic refresh map field via crafted image pixel values. The encoder then writes approximately 1,200 bytes at the attacker-controlled address. This is fully deterministic and does not require a separate information leak. An attacker who can supply frames to a network-facing libaom encoder with SVC enabled could exploit this for denial of service or potential code execution.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "libaom: libaom: arbitrary address write via SVC layer context OOB and cyclic refresh map pointer hijack",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability is rated as Critical severity because it provides a fully deterministic arbitrary address write primitive that requires no information leak and is self-bootstrapping from attacker-controlled pixel values. The 1,200-byte write at an attacker-chosen address is sufficient for control flow hijacking. In Red Hat products, libaom ships bundled within Firefox and Thunderbird. The vulnerable code path requires the SVC (Scalable Video Coding) encoder feature to be enabled and the attacker to control both the layer_id configuration and the image frame pixel values. In Firefox\u0027s WebRTC implementation, SVC encoding parameters and frame submission are managed internally by the browser; a remote peer cannot directly set arbitrary layer IDs or inject pixel values into the local encoder. This significantly reduces exploitability in the browser context. RHEL-AI 3.4 (aom 3.12.0) and Hummingbird 1 (aom 3.13.3) ship standalone libaom packages within the affected version range. Services on those platforms that expose the SVC encoder API with attacker-controlled layer configuration and frame input are at highest risk.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
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          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-56209"
        },
        {
          "category": "external",
          "summary": "RHBZ#2490800",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2490800"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-56209",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-56209"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-56209",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-56209"
        },
        {
          "category": "external",
          "summary": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa",
          "url": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa"
        },
        {
          "category": "external",
          "summary": "https://issues.chromium.org/issues/503993984",
          "url": "https://issues.chromium.org/issues/503993984"
        }
      ],
      "release_date": "2026-06-19T00:00:00+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "There is no complete mitigation for this vulnerability. The following measures can reduce risk:\n\n1. If using libaom as a standalone encoder library with SVC enabled, validate that spatial_layer_id and temporal_layer_id values are within the configured range [0, configured_layers) before calling aom_codec_control with AV1E_SET_SVC_LAYER_ID.\n2. Restrict access to encoding services to trusted clients only. Do not expose libaom SVC encoder configuration to untrusted input.\n3. For Firefox and Thunderbird, ensure browsers are updated to versions that include the patched libaom (v3.14.0 or later).\n4. Deploy encoding services with ASLR, stack canaries, and other exploit mitigation technologies enabled.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "libaom: libaom: arbitrary address write via SVC layer context OOB and cyclic refresh map pointer hijack"
    },
    {
      "acknowledgments": [
        {
          "names": [
            "The FuzzAnything Team"
          ],
          "organization": "FuzzAnything"
        }
      ],
      "cve": "CVE-2026-56210",
      "cwe": {
        "id": "CWE-125",
        "name": "Out-of-bounds Read"
      },
      "discovery_date": "2026-06-19T00:00:00+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2490801"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A heap-buffer-overflow read vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows setting a spatial_layer_id exceeding the configured number of layers. This causes an out-of-bounds heap read of approximately 40,728 bytes when computing a layer context array index. An attacker who can influence SVC encoder parameters in a network-facing service could exploit this for information disclosure (heap content leak) or denial of service (segmentation fault from hitting unmapped memory).",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "libaom: libaom: heap-buffer-overflow read via missing bounds check in ctrl_set_layer_id",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability is rated as Important severity because the 40KB out-of-bounds heap read can disclose sensitive information from adjacent heap allocations (including pointers useful for ASLR bypass in chained attacks) and reliably causes denial of service by hitting unmapped pages. In Red Hat products, libaom ships bundled within Firefox and Thunderbird. The vulnerable code path requires the SVC encoder feature to be enabled and an attacker to set spatial_layer_id to a value exceeding the number of configured spatial layers. In Firefox\u0027s WebRTC implementation, SVC layer parameters are managed internally by the browser and not directly exposed to remote peers, which limits exploitability. RHEL-AI 3.4 (aom 3.12.0) and Hummingbird 1 (aom 3.13.3) ship standalone libaom packages within the affected version range. Services that expose SVC encoder layer configuration to untrusted input are affected.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-56210"
        },
        {
          "category": "external",
          "summary": "RHBZ#2490801",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2490801"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-56210",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-56210"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-56210",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-56210"
        },
        {
          "category": "external",
          "summary": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa",
          "url": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa"
        },
        {
          "category": "external",
          "summary": "https://issues.chromium.org/issues/503975732",
          "url": "https://issues.chromium.org/issues/503975732"
        }
      ],
      "release_date": "2026-06-19T00:00:00+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "There is no complete mitigation for this vulnerability. The following measures can reduce risk:\n\n1. If using libaom as a standalone encoder library with SVC enabled, validate that spatial_layer_id does not exceed the number of configured spatial layers before calling aom_codec_control with AV1E_SET_SVC_LAYER_ID.\n2. Restrict access to encoding services to trusted clients only.\n3. For Firefox and Thunderbird, ensure browsers are updated to versions that include the patched libaom (v3.14.0 or later).\n4. Monitor encoding service processes for unexpected crashes (segfaults) that may indicate exploitation attempts.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "libaom: libaom: heap-buffer-overflow read via missing bounds check in ctrl_set_layer_id"
    },
    {
      "acknowledgments": [
        {
          "names": [
            "The FuzzAnything Team"
          ],
          "organization": "FuzzAnything"
        }
      ],
      "cve": "CVE-2026-56211",
      "cwe": {
        "id": "CWE-787",
        "name": "Out-of-bounds Write"
      },
      "discovery_date": "2026-06-19T00:00:00+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2490802"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A remote code execution vulnerability was found in libaom, the reference AV1 codec implementation. Insufficient bounds validation in the AV1 encoder\u0027s SVC (Scalable Video Coding) layer ID control allows an attacker to supply crafted video frame pixels that overlap with internal encoder layer context structures. In fork-based video processing services, an attacker can use this to hijack the cyclic refresh map pointer, brute-force the process base address via a crash oracle, and redirect control flow to achieve arbitrary command execution. Exploitation requires the target service to use libaom with SVC encoding enabled and accept attacker-supplied video frames.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "libaom: libaom: remote code execution via SVC layer context handling with attacker-controlled frames",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This vulnerability is rated as Critical severity because the researcher demonstrated successful remote code execution against a fork-based video processing service. The exploit chain leverages attacker-controlled pixel values to hijack internal encoder pointers, uses a crash oracle to brute-force ASLR, and ultimately achieves arbitrary command execution. However, the attack complexity is elevated: it requires a fork-based service architecture (for the crash oracle), multiple encoding attempts (for ASLR brute-force), and knowledge of the target binary layout. In Red Hat products, libaom ships bundled within Firefox and Thunderbird. Firefox does not use a fork-based architecture for WebRTC encoding, and SVC layer parameters are managed internally, making the demonstrated exploit chain not directly applicable to the browser context. RHEL-AI 3.4 (aom 3.12.0) and Hummingbird 1 (aom 3.13.3) ship standalone libaom packages within the affected version range. Fork-based transcoding or video conferencing services that use libaom with SVC encoding and accept attacker-supplied frames are at highest risk for this specific exploit chain.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-56211"
        },
        {
          "category": "external",
          "summary": "RHBZ#2490802",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2490802"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-56211",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-56211"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-56211",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-56211"
        },
        {
          "category": "external",
          "summary": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa",
          "url": "https://aomedia.googlesource.com/aom/+/a93ba0ffaa"
        },
        {
          "category": "external",
          "summary": "https://issues.chromium.org/issues/503993985",
          "url": "https://issues.chromium.org/issues/503993985"
        }
      ],
      "release_date": "2026-06-19T00:00:00+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "There is no complete mitigation for this vulnerability. The following measures can reduce risk:\n\n1. If using libaom as a standalone encoder in a fork-based service, validate all SVC layer parameters (spatial_layer_id, temporal_layer_id) against configured bounds before passing them to the encoder API.\n2. Avoid fork-based architectures for encoding services that accept untrusted input. Use thread-based or container-isolated workers instead, which prevent crash oracle attacks.\n3. Restrict access to encoding services to trusted clients only. Do not expose SVC encoder configuration or frame submission to untrusted network input.\n4. For Firefox and Thunderbird, ensure browsers are updated to versions that include the patched libaom (v3.14.0 or later).\n5. Enable all available exploit mitigations (ASLR, PIE, stack canaries, CFI) on encoding service binaries.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:L/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "libaom: libaom: remote code execution via SVC layer context handling with attacker-controlled frames"
    },
    {
      "cve": "CVE-2026-57516",
      "cwe": {
        "id": "CWE-502",
        "name": "Deserialization of Untrusted Data"
      },
      "discovery_date": "2026-07-01T18:01:18.327453+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2496087"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Ray. This unsafe deserialization vulnerability in the WebDataset reader allows a remote attacker to achieve arbitrary code execution. By supplying a specially crafted malicious tar archive to the read_webdataset() function, an attacker can trigger the unconditional deserialization of .pkl/.pickle or .pt/.pth entries, leading to the execution of arbitrary code within Ray remote workers.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "ray: Ray: Remote code execution via unsafe deserialization in WebDataset reader",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Important vulnerability in Ray\u0027s WebDataset reader allows for remote code execution. An attacker could exploit this by providing a specially crafted tar archive to the `read_webdataset()` function, leading to arbitrary code execution within Ray remote workers. This risk is present in Red Hat environments where Ray is deployed to process untrusted data archives, as the deserialization of malicious content occurs unconditionally.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-57516"
        },
        {
          "category": "external",
          "summary": "RHBZ#2496087",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2496087"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-57516",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-57516"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-57516",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-57516"
        },
        {
          "category": "external",
          "summary": "https://github.com/ray-project/ray/pull/63469",
          "url": "https://github.com/ray-project/ray/pull/63469"
        },
        {
          "category": "external",
          "summary": "https://github.com/ray-project/ray/pull/63470",
          "url": "https://github.com/ray-project/ray/pull/63470"
        },
        {
          "category": "external",
          "summary": "https://github.com/ray-project/ray/releases/tag/ray-2.56.0",
          "url": "https://github.com/ray-project/ray/releases/tag/ray-2.56.0"
        },
        {
          "category": "external",
          "summary": "https://github.com/ray-project/ray/security/advisories/GHSA-hhrp-gw25-jr43",
          "url": "https://github.com/ray-project/ray/security/advisories/GHSA-hhrp-gw25-jr43"
        },
        {
          "category": "external",
          "summary": "https://www.vulncheck.com/advisories/ray-unsafe-deserialization-rce-via-webdataset-reader",
          "url": "https://www.vulncheck.com/advisories/ray-unsafe-deserialization-rce-via-webdataset-reader"
        }
      ],
      "release_date": "2026-07-01T16:36:55.765000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "To mitigate this issue, restrict the processing of untrusted tar archives by the Ray WebDataset reader. Ensure that only verified and trusted data sources are supplied to the `read_webdataset()` function. Implement strict access controls and input validation for data ingestion pipelines that interact with Ray\u0027s WebDataset reader to prevent the introduction of malicious archives.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.8,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "ray: Ray: Remote code execution via unsafe deserialization in WebDataset reader"
    },
    {
      "cve": "CVE-2026-58049",
      "cwe": {
        "id": "CWE-787",
        "name": "Out-of-bounds Write"
      },
      "discovery_date": "2026-06-28T03:01:15.644058+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2493952"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in FFmpeg\u0027s RASC video decoder. A remote attacker could exploit this by providing a crafted media stream using the RASC FourCC (Four Character Code), which is then decoded by libavcodec. This vulnerability triggers a bitstream-controlled out-of-bounds heap write and an adjacent out-of-bounds read, leading to memory corruption. This can result in a denial of service or potentially arbitrary code execution.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "FFmpeg: FFmpeg: Memory corruption via crafted RASC video stream",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "The vulnerability in FFmpeg\u0027s RASC video decoder is rated as Important. This flaw allows a remote attacker to cause memory corruption, potentially leading to denial of service or arbitrary code execution, by providing a specially crafted media stream. Red Hat products utilizing FFmpeg for RASC video decoding are at risk if they process untrusted media content.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-58049"
        },
        {
          "category": "external",
          "summary": "RHBZ#2493952",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2493952"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-58049",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-58049"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-58049",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-58049"
        },
        {
          "category": "external",
          "summary": "https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/rasc.c",
          "url": "https://github.com/FFmpeg/FFmpeg/blob/master/libavcodec/rasc.c"
        },
        {
          "category": "external",
          "summary": "https://github.com/bikini/exploitarium/tree/main/ffmpeg-rasc-dlta-calc-poc",
          "url": "https://github.com/bikini/exploitarium/tree/main/ffmpeg-rasc-dlta-calc-poc"
        },
        {
          "category": "external",
          "summary": "https://www.vulncheck.com/advisories/ffmpeg-out-of-bounds-write-in-rasc-decoder-decode-dlta",
          "url": "https://www.vulncheck.com/advisories/ffmpeg-out-of-bounds-write-in-rasc-decoder-decode-dlta"
        }
      ],
      "release_date": "2026-06-28T01:32:52.900000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.6,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "FFmpeg: FFmpeg: Memory corruption via crafted RASC video stream"
    },
    {
      "cve": "CVE-2026-59204",
      "cwe": {
        "id": "CWE-131",
        "name": "Incorrect Calculation of Buffer Size"
      },
      "discovery_date": "2026-07-14T16:02:11.802781+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2500022"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. A remote attacker could exploit this vulnerability by providing a specially crafted JPEG2000 image file. Due to incorrect calculation of memory requirements for image tiles, processing this file can lead to excessive memory consumption, resulting in a denial of service (DoS) through out-of-memory failures.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "Pillow: Pillow: Denial of Service via crafted JPEG2000 image",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "The impact is limited to denial of service via excessive memory allocation. A crafted JPEG2000 file can trigger out-of-memory failures, but this does not lead to code execution or information disclosure.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-59204"
        },
        {
          "category": "external",
          "summary": "RHBZ#2500022",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500022"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-59204",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-59204"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59204",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59204"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/13ada41172142f2fd9f0906f615a00ea623a11ca",
          "url": "https://github.com/python-pillow/Pillow/commit/13ada41172142f2fd9f0906f615a00ea623a11ca"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/pull/9704",
          "url": "https://github.com/python-pillow/Pillow/pull/9704"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
          "url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-vjc4-5qp5-m44j",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-vjc4-5qp5-m44j"
        }
      ],
      "release_date": "2026-07-14T15:38:29.545000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "If the application does not need JPEG2000 support, block .jp2, .j2k, .jpf, and .jpx uploads at the input layer.\n\nFor services that do process JPEG2000, set memory limits on the process or container (LimitAS= in systemd, or memory limits in Kubernetes/Podman) so a crafted image can only crash the worker, not the whole host. Add automatic restarts (Restart=always in systemd, or container restart policies) so the service recovers from OOM kills without someone having to intervene.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "Pillow: Pillow: Denial of Service via crafted JPEG2000 image"
    },
    {
      "cve": "CVE-2026-59205",
      "cwe": {
        "id": "CWE-843",
        "name": "Access of Resource Using Incompatible Type (\u0027Type Confusion\u0027)"
      },
      "discovery_date": "2026-07-14T17:02:11.367224+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2500052"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. This vulnerability allows an attacker to trigger controlled native heap corruption by supplying an output image whose mode does not match the transform\u0027s declared output mode when using the ImageCms.ImageCmsTransform.apply API. This can lead to a denial of service (DoS) condition, making the affected system or application unavailable.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "The impact is limited to denial of service. While the underlying issue is heap corruption via mode mismatch, it does not provide a controlled write primitive exploitable for code execution in affected Red Hat products.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-59205"
        },
        {
          "category": "external",
          "summary": "RHBZ#2500052",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2500052"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-59205",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-59205"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-59205"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721",
          "url": "https://github.com/python-pillow/Pillow/commit/a9ffc42bedf4fc0a7ef8d6486e7f9e81e3397721"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/pull/9715",
          "url": "https://github.com/python-pillow/Pillow/pull/9715"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0",
          "url": "https://github.com/python-pillow/Pillow/releases/tag/12.3.0"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-9hw9-ch79-4vh6",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-9hw9-ch79-4vh6"
        }
      ],
      "release_date": "2026-07-14T15:48:39.962000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "Most applications using Pillow\u0027s color management via profileToProfile() or applyTransform() are not exposed. Only code that calls ImageCmsTransform.apply() directly with a user-controlled output image whose mode does not match the transform can trigger the heap corruption. Audit your code for direct apply() calls to confirm.\n\nRHEL builds ship with ASLR, full RELRO/PIE, and FORTIFY_SOURCE by default, making escalation from crash to code execution much harder. For DoS containment, configure automatic service restart (Restart=always in systemd, or container restart policies) so the process recovers without manual intervention.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "Pillow: Pillow: Controlled native heap corruption in ImageCms.ImageCmsTransform.apply API"
    },
    {
      "cve": "CVE-2026-64830",
      "cwe": {
        "id": "CWE-787",
        "name": "Out-of-bounds Write"
      },
      "discovery_date": "2026-07-22T18:01:30.962327+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2506193"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in FFmpeg, a widely used multimedia framework. This vulnerability, a heap buffer overflow in the VobSub subtitle demuxer, allows a remote attacker to corrupt memory. By providing a specially crafted subtitle file, an attacker can trigger unbounded writes, which could lead to arbitrary code execution in applications that use FFmpeg\u0027s VobSub demuxer. This could allow an attacker to take control of the affected system.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "ffmpeg: FFmpeg: Arbitrary code execution via heap buffer overflow in VobSub subtitle demuxer.",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "A heap buffer overflow in FFmpeg\u0027s VobSub subtitle demuxer can lead to arbitrary code execution when processing a specially crafted subtitle file. Exploitation requires user interaction, such as opening a malicious media file, which reduces the attack surface but can result in high impact if an affected application processes untrusted content.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-64830"
        },
        {
          "category": "external",
          "summary": "RHBZ#2506193",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2506193"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-64830",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-64830"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-64830",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64830"
        },
        {
          "category": "external",
          "summary": "https://code.ffmpeg.org/FFmpeg/FFmpeg/commit/dbd495f066a85ba96b17433f4306582aa37c3951",
          "url": "https://code.ffmpeg.org/FFmpeg/FFmpeg/commit/dbd495f066a85ba96b17433f4306582aa37c3951"
        },
        {
          "category": "external",
          "summary": "https://code.ffmpeg.org/FFmpeg/FFmpeg/pulls/23657",
          "url": "https://code.ffmpeg.org/FFmpeg/FFmpeg/pulls/23657"
        },
        {
          "category": "external",
          "summary": "https://www.vulncheck.com/advisories/ffmpeg-heap-buffer-overflow-via-vobsub-subtitle-demuxer",
          "url": "https://www.vulncheck.com/advisories/ffmpeg-heap-buffer-overflow-via-vobsub-subtitle-demuxer"
        }
      ],
      "release_date": "2026-07-22T16:33:05.724000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        },
        {
          "category": "workaround",
          "details": "To mitigate this issue, avoid processing VobSub subtitle files from untrusted or unknown sources. For applications that utilize FFmpeg\u0027s VobSub demuxer, consider running them within a sandboxed environment to restrict the potential impact of a successful exploit.",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
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      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.8,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "ffmpeg: FFmpeg: Arbitrary code execution via heap buffer overflow in VobSub subtitle demuxer."
    },
    {
      "cve": "CVE-2026-64835",
      "cwe": {
        "id": "CWE-125",
        "name": "Out-of-bounds Read"
      },
      "discovery_date": "2026-07-22T18:01:43.659646+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2506195"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in FFmpeg. A remote attacker could exploit an out-of-bounds memory access vulnerability in the ADX audio decoder by supplying a specially crafted ADX or AAX audio file. This could lead to the attacker triggering both out-of-bounds reads and writes, potentially resulting in arbitrary code execution, information disclosure, or denial of service.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "FFmpeg: FFmpeg: Arbitrary code execution, information disclosure, or denial of service via crafted ADX/AAX audio files",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "Conditions for Exploitation: Successful exploitation requires user interaction or a specific application workflow. An attacker cannot trigger this vulnerability entirely remotely on their own; they must supply a specially crafted ADX or AAX audio file and rely on a user or an automated media-processing system to actively parse it using the FFmpeg library.\n\nImpact Limitations: Although the out-of-bounds read and write vulnerabilities have the potential to allow for arbitrary code execution or information disclosure, achieving this reliably is highly complex and depends on the memory layout and protections of the specific application utilizing FFmpeg. In most typical scenarios, processing the malformed file will simply cause the parsing application to crash, resulting in a localized Denial of Service (DoS) rather than a full system compromise.",
          "title": "Statement"
        },
        {
          "category": "general",
          "text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
          "title": "CVSS score applicability"
        }
      ],
      "product_status": {
        "fixed": [
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
          "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-64835"
        },
        {
          "category": "external",
          "summary": "RHBZ#2506195",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2506195"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-64835",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-64835"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-64835",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64835"
        },
        {
          "category": "external",
          "summary": "https://code.ffmpeg.org/FFmpeg/FFmpeg/commit/1836ef96846937a6cc2443698a693104f5c0b21e",
          "url": "https://code.ffmpeg.org/FFmpeg/FFmpeg/commit/1836ef96846937a6cc2443698a693104f5c0b21e"
        },
        {
          "category": "external",
          "summary": "https://code.ffmpeg.org/FFmpeg/FFmpeg/pulls/23659",
          "url": "https://code.ffmpeg.org/FFmpeg/FFmpeg/pulls/23659"
        },
        {
          "category": "external",
          "summary": "https://www.vulncheck.com/advisories/ffmpeg-out-of-bounds-memory-access-in-adx-audio-decoder",
          "url": "https://www.vulncheck.com/advisories/ffmpeg-out-of-bounds-memory-access-in-adx-audio-decoder"
        }
      ],
      "release_date": "2026-07-22T17:24:05.477000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-31T13:38:30+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:61627",
          "product_ids": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:61627"
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.8,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64",
            "Red Hat AI Inference Server 3.2:registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "FFmpeg: FFmpeg: Arbitrary code execution, information disclosure, or denial of service via crafted ADX/AAX audio files"
    }
  ]
}