Vulnerability-Lookup

RHSA-2026:60363

Vulnerability from csaf_redhat - Published: 2026-08-26 16:25 - Updated: 2026-08-26 16:44

Summary

Red Hat Security Advisory: Red Hat AI Inference Server 3.3.6 (Spyre)

Severity

Important

Notes

Topic: Red Hat AI Inference Server 3.3.6 (Spyre) 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-2026-44222

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.

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CWE-1285 - Improper Validation of Specified Index, Position, or Offset in Input

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64	—	Vendor Fix fix Workaround

Threats

Impact Low

CVE-2026-54234

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.

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CWE-125 - Out-of-bounds Read

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64	—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-55574

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.

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

CWE-1333 - Inefficient Regular Expression Complexity

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64	—	Vendor Fix fix Workaround

Threats

Impact Important

References

44 references

URL	Category
https://access.redhat.com/errata/RHSA-2026:60363	self
https://access.redhat.com/security/cve/CVE-2026-44222	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/cve/CVE-2026-54234	external
https://access.redhat.com/security/cve/CVE-2026-55574	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-2026-44222	self
https://bugzilla.redhat.com/show_bug.cgi?id=2476840	external
https://www.cve.org/CVERecord?id=CVE-2026-44222	external
https://nvd.nist.gov/vuln/detail/CVE-2026-44222	external
https://github.com/vllm-project/vllm/issues/32656	external
https://github.com/vllm-project/vllm/security/adv…	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
https://access.redhat.com/security/cve/CVE-2026-54234	self
https://bugzilla.redhat.com/show_bug.cgi?id=2497515	external
https://www.cve.org/CVERecord?id=CVE-2026-54234	external
https://nvd.nist.gov/vuln/detail/CVE-2026-54234	external
https://github.com/vllm-project/vllm/commit/8a5cf…	external
https://github.com/vllm-project/vllm/pull/44744	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-55574	self
https://bugzilla.redhat.com/show_bug.cgi?id=2497509	external
https://www.cve.org/CVERecord?id=CVE-2026-55574	external
https://nvd.nist.gov/vuln/detail/CVE-2026-55574	external
https://github.com/vllm-project/vllm/commit/2b300…	external
https://github.com/vllm-project/vllm/pull/45118	external
https://github.com/vllm-project/vllm/security/adv…	external

JSON

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          "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": [
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          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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": "2026-08-26T16:25:47+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:60363"
        },
        {
          "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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed multimodal input or token injection"
    },
    {
      "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": {
        "fixed": [
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          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "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"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6",
          "url": "https://github.com/vllm-project/vllm/commit/d26a28ab033697f55a1414b5b0435de7cd6045b6"
        },
        {
          "category": "external",
          "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"
        },
        {
          "category": "external",
          "summary": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac",
          "url": "https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac"
        }
      ],
      "release_date": "2026-06-22T22:20:10.793000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-26T16:25:47+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
          "product_ids": [
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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:60363"
        },
        {
          "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-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
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      ],
      "scores": [
        {
          "cvss_v3": {
            "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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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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": [
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          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
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          "summary": "Canonical URL",
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        },
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          "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"
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      "release_date": "2026-06-22T21:55:42.001000+00:00",
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          "date": "2026-08-26T16:25:47+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
          "product_ids": [
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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
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          ],
          "restart_required": {
            "category": "none"
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          "url": "https://access.redhat.com/errata/RHSA-2026:60363"
        },
        {
          "category": "workaround",
          "details": "No mitigation is required for unaffected deployments. Restrict untrusted access to inference APIs as a general hardening measure.",
          "product_ids": [
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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
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      "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": [
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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Low"
        }
      ],
      "title": "vllm: vLLM: Information disclosure via integer truncation"
    },
    {
      "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"
        }
      ],
      "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": {
        "fixed": [
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-54234"
        },
        {
          "category": "external",
          "summary": "RHBZ#2497515",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497515"
        },
        {
          "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": "2026-08-26T16:25:47+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
          "product_ids": [
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:60363"
        },
        {
          "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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed speculative decoding workload"
    },
    {
      "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"
        }
      ],
      "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 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": {
        "fixed": [
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
          "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
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        ]
      },
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        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-55574"
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          "category": "external",
          "summary": "RHBZ#2497509",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2497509"
        },
        {
          "category": "external",
          "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"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/commit/2b3006076c5e9bc4cda9e03e3641388de3c5c286",
          "url": "https://github.com/vllm-project/vllm/commit/2b3006076c5e9bc4cda9e03e3641388de3c5c286"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/pull/45118",
          "url": "https://github.com/vllm-project/vllm/pull/45118"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-rwxx-mrjm-wc2m",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-rwxx-mrjm-wc2m"
        }
      ],
      "release_date": "2026-07-06T20:05:31.003000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-26T16:25:47+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:60363",
          "product_ids": [
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            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:60363"
        },
        {
          "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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_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 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:590f1bb37f9c9abb51a6ff7f557b45f1dcfba2e4880de979290703c2e6de95be_s390x",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:e54618292d1e6f1c9c959a42160bad3a153ca7e050665739d3ddd9dbfda541f8_ppc64le",
            "Red Hat AI Inference Server 3.3:registry.redhat.io/rhaiis/vllm-spyre-rhel9@sha256:f3dfb688e524f44f071f20954e454a91013a9c48f8cc59a32f7f402bb61d8ed0_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via adversarial regular expression in structured outputs API"
    }
  ]
}

CVE-2026-44222 (GCVE-0-2026-44222)

Vulnerability from cvelistv5 – Published: 2026-05-12 19:57 – Updated: 2026-05-13 12:24

Title

vLLM: Remote DoS via Special-Token Placeholders

Summary

vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there is a a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on image_grid_thw/video_grid_thw are affected. This vulnerability is fixed in 0.20.0.

Severity

6.5 (Medium)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-05-13 12:24 UTC

CWE

CWE-129 - Improper Validation of Array Index

Assigner

GitHub_M mitre root

References

2 references

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/issues/32656	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
vllm-project	vllm	Affected: >= 0.6.1, < 0.20.0

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
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              "content": {
                "id": "CVE-2026-44222",
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      "metrics": [
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          "cvssV3_1": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
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      "title": "vLLM: Remote DoS via Special-Token Placeholders"
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CVE-2026-47155 (GCVE-0-2026-47155)

Vulnerability from cvelistv5 – Published: 2026-06-22 22:20 – Updated: 2026-06-23 12:35

Title

vLLM: Artifact Pin Decay in vLLM allows pinned deployments to load unpinned code, weights, and processors

Summary

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.

Severity

6.5 (Medium)


                        
                          CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:H/A:N

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-23 12:34 UTC

CWE

CWE-345 - Insufficient Verification of Data Authenticity

Assigner

GitHub_M mitre root

References

4 references

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/42616	x_refsource_MISC
https://github.com/vllm-project/vllm/commit/d26a2…	x_refsource_MISC
https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a70…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
vllm-project	vllm	Affected: < 0.22.0

Show details on NVD website

JSON

To clipboard

{
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    "adp": [
      {
        "metrics": [
          {
            "other": {
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                "id": "CVE-2026-47155",
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                    "Technical Impact": "partial"
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                "timestamp": "2026-06-23T12:34:39.566846Z",
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CVE-2026-53923 (GCVE-0-2026-53923)

Vulnerability from cvelistv5 – Published: 2026-06-22 21:55 – Updated: 2026-06-23 15:05

Title

vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow

Summary

vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.

Severity

5.3 (Medium)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-23 15:04 UTC

CWE

CWE-681 - Incorrect Conversion between Numeric Types
CWE-200 - Exposure of Sensitive Information to an Unauthorized Actor

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/44971	x_refsource_MISC
https://github.com/vllm-project/vllm/commit/f2197…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
vllm-project	vllm	Affected: >= 0.5.5, < 0.23.1rc0

Show details on NVD website

JSON

To clipboard

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CVE-2026-54234 (GCVE-0-2026-54234)

Vulnerability from cvelistv5 – Published: 2026-07-06 19:49 – Updated: 2026-07-07 14:13

Title

vLLM: Remote DoS in vLLM via Invalid Recovered Token Reinjection

Summary

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, a frontend-legal multi-request speculative decoding workload can cause the rejection sampler to produce a recovered token equal to the model vocabulary size boundary value, which is then converted to negative one when the engine selects the next live token for a request and is written back into the drafter's input ids; that out-of-vocabulary value is later consumed by the model's embedding and attention path and crashes the engine worker with a GPU device-side assertion. The same triggering request sequence is reachable through the public gRPC Generate and Abort endpoints, so a remote client that can send generation requests can crash the shared engine worker, aborting concurrent requests and causing a service-wide denial of service for other clients of the deployment until the worker is restarted. This issue is fixed in version 0.24.0.

Severity

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

SSVC

Exploitation: poc Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-07-07 14:12 UTC

CWE

CWE-20 - Improper Input Validation
CWE-1284 - Improper Validation of Specified Quantity in Input

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/44744	x_refsource_MISC
https://github.com/vllm-project/vllm/commit/8a5cf…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
vllm-project	vllm	Affected: < 0.24.0

Show details on NVD website

JSON

To clipboard

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CVE-2026-55574 (GCVE-0-2026-55574)

Vulnerability from cvelistv5 – Published: 2026-07-06 20:05 – Updated: 2026-07-06 20:52

Title

vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends

Summary

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Prior to 0.24.0, the structured_outputs.regex API parameter passes a user-supplied regular expression string directly to the grammar compiler backends with no compilation timeout; in the xgrammar backend the string reaches the regex compiler with no guard, and in the outlines backend the validation step blocks structural issues such as lookarounds and backreferences but performs no complexity analysis, so a pattern with nested quantifiers passes all checks and causes exponential state-space expansion, allowing a single request containing an adversarial regex to hang an inference worker indefinitely and deny service. This issue is fixed in version 0.24.0.

Severity

8.7 (High)


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

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-07-06 20:52 UTC

CWE

CWE-1333 - Inefficient Regular Expression Complexity

Assigner

GitHub_M mitre root

References

3 references

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/45118	x_refsource_MISC
https://github.com/vllm-project/vllm/commit/2b300…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
vllm-project	vllm	Affected: < 0.24.0

Show details on NVD website

JSON

To clipboard

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  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2026-55574",
    "datePublished": "2026-07-06T20:05:31.003Z",
    "dateReserved": "2026-06-16T23:11:20.214Z",
    "dateUpdated": "2026-07-06T20:52:57.336Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2"
}

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.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.

Action not permitted

RHSA-2026:60363

CVE-2026-44222 (GCVE-0-2026-44222)

CVE-2026-47155 (GCVE-0-2026-47155)

CVE-2026-53923 (GCVE-0-2026-53923)

CVE-2026-54234 (GCVE-0-2026-54234)

CVE-2026-55574 (GCVE-0-2026-55574)

Tags

Sightings

Nomenclature