RHSA-2026:59139

Vulnerability from csaf_redhat - Published: 2026-08-24 16:51 - Updated: 2026-08-25 09:38
Summary
Red Hat Security Advisory: Red Hat AI Inference Server 3.3.6 (ROCm)
Severity
Important
Notes
Topic: Red Hat AI Inference Server 3.3.6 (ROCm) is now available.
Details: Red Hat® AI Inference Server
Terms of Use: 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.
CVE-2026-41523

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.

7.5 (High)
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
CWE-617 - Reachable Assertion
Affected products
Product Identifier Version Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 —
Vendor Fix fix
Workaround
Threats
Impact Important
CVE-2026-47155

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.

6.5 (Medium)
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N
CWE-829 - Inclusion of Functionality from Untrusted Control Sphere
Affected products
Product Identifier Version Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 —
Vendor Fix fix
Workaround
Threats
Impact Moderate
CVE-2026-53923

A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's 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' requests. This constitutes an information disclosure vulnerability.

4.3 (Medium)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N
CWE-824 - Access of Uninitialized Pointer
Affected products
Product Identifier Version Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64 —
Vendor Fix fix
Workaround
Threats
Impact Low
References
URL Category
https://access.redhat.com/errata/RHSA-2026:59139 self
https://access.redhat.com/security/cve/CVE-2026-41523 external
https://access.redhat.com/security/cve/CVE-2026-47155 external
https://access.redhat.com/security/cve/CVE-2026-53923 external
https://access.redhat.com/security/updates/classi… external
https://www.redhat.com/en/products/ai/inference-server external
https://security.access.redhat.com/data/csaf/v2/a… self
https://access.redhat.com/security/cve/CVE-2026-41523 self
https://bugzilla.redhat.com/show_bug.cgi?id=2491582 external
https://www.cve.org/CVERecord?id=CVE-2026-41523 external
https://nvd.nist.gov/vuln/detail/CVE-2026-41523 external
https://github.com/vllm-project/vllm/commit/b3c7f… external
https://github.com/vllm-project/vllm/security/adv… external
https://huntr.com/bounties/dcb05b04-e625-41e7-adb… external
https://access.redhat.com/security/cve/CVE-2026-47155 self
https://bugzilla.redhat.com/show_bug.cgi?id=2491580 external
https://www.cve.org/CVERecord?id=CVE-2026-47155 external
https://nvd.nist.gov/vuln/detail/CVE-2026-47155 external
https://github.com/vllm-project/vllm/commit/d26a2… external
https://github.com/vllm-project/vllm/pull/42616 external
https://github.com/vllm-project/vllm/security/adv… external
https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a70… external
https://access.redhat.com/security/cve/CVE-2026-53923 self
https://bugzilla.redhat.com/show_bug.cgi?id=2491579 external
https://www.cve.org/CVERecord?id=CVE-2026-53923 external
https://nvd.nist.gov/vuln/detail/CVE-2026-53923 external
https://github.com/vllm-project/vllm/commit/f2197… external
https://github.com/vllm-project/vllm/pull/44971 external
https://github.com/vllm-project/vllm/security/adv… external
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          "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.",
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          "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.",
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      "cwe": {
        "id": "CWE-829",
        "name": "Inclusion of Functionality from Untrusted Control Sphere"
      },
      "discovery_date": "2026-06-22T23:00:54.705583+00:00",
      "ids": [
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          "text": "2491580"
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          "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"
        }
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            "baseScore": 6.5,
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            "confidentialityImpact": "LOW",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
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            "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": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ]
        }
      ],
      "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": {
        "fixed": [
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-53923"
        },
        {
          "category": "external",
          "summary": "RHBZ#2491579",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2491579"
        },
        {
          "category": "external",
          "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"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e",
          "url": "https://github.com/vllm-project/vllm/commit/f219788f91952827132fa4fdf916427cd20d225e"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/44971",
          "url": "https://github.com/vllm-project/vllm/pull/44971"
        },
        {
          "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"
        }
      ],
      "release_date": "2026-06-22T21:55:42.001000+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": "No mitigation is required for unaffected deployments. Restrict untrusted access to inference APIs as a general hardening measure.",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "NONE",
            "baseScore": 4.3,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "LOW",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:N",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:dbce78adf45d71b4348c55a3aa1dd9327ea7ec726cf0e5487246de180ecf8a3e_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Low"
        }
      ],
      "title": "vllm: vLLM: Information disclosure via integer truncation"
    }
  ]
}



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Taxonomy of the tags.





Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.


Detection rules are retrieved from Rulezet.