Vulnerability-Lookup

URL	Tags
https://github.com/vllm-project/vllm/security/adv…	x_refsource_CONFIRM
https://github.com/vllm-project/vllm/pull/38610	x_refsource_MISC

Vendor	Product	Version	CPE status
vllm-project	vllm	Affected: >= 0.18.0, < 0.20.0	guessed

FKIE_CVE-2026-44223

Vulnerability from fkie_nvd - Published: 2026-05-12 20:16 - Updated: 2026-06-22 22:16

Severity

6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Summary

vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.

References

URL	Tags
security-advisories@github.com	https://github.com/vllm-project/vllm/pull/38610	Issue Tracking, Patch
security-advisories@github.com	https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw	Mitigation, Vendor Advisory
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://github.com/vllm-project/vllm/pull/38610	Issue Tracking, Patch
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw	Mitigation, Vendor Advisory

Impacted products

	Vendor	Product	Version
	vllm	vllm	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "vllm",
          "vendor": "vllm-project",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 0.18.0, \u003c 0.20.0"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "443F32C2-B323-4FA0-AB97-F6445C91C89E",
              "versionEndExcluding": "0.20.0",
              "versionStartIncluding": "0.18.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., \"repetition_penalty\": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0."
    }
  ],
  "id": "CVE-2026-44223",
  "lastModified": "2026-06-22T22:16:45.507",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "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",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 3.6,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-44223",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-05-15T14:44:05.012494Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-05-12T20:16:43.293",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/vllm-project/vllm/pull/38610"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Mitigation",
        "Vendor Advisory"
      ],
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/vllm-project/vllm/pull/38610"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Mitigation",
        "Vendor Advisory"
      ],
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-131"
        },
        {
          "lang": "en",
          "value": "CWE-704"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

GHSA-83VM-P52W-F9PW

Vulnerability from github – Published: 2026-05-06 21:45 – Updated: 2026-06-08 19:52

Summary

vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters

Details

Summary

The extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty).

A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. The crash is deterministic and immediate — no concurrency, race condition, or special workload is required.

Details

In vLLM v0.17.0, the extract_hidden_states proposer's propose() method returned sampled_token_ids.unsqueeze(-1), producing a tensor of shape (batch_size, 1).

In PR #37013 (first released in v0.18.0), the KV connector interface was refactored out of propose(). The return type changed from tuple[Tensor, KVConnectorOutput | None] to Tensor, and the .unsqueeze(-1) call was removed along with the KV connector output:

# Before (v0.17.0):
return sampled_token_ids.unsqueeze(-1), kv_connector_output  # shape (batch_size, 1)

# After (v0.18.0+):
return sampled_token_ids  # shape (batch_size, 2) after first decode step

The refactor missed that sampled_token_ids changed semantics between the first and subsequent decode steps. After the first decode step, the rejection sampler allocates its output as (batch_size, max_spec_len + 1). With num_speculative_tokens=1, this produces shape (batch_size, 2) instead of the expected (batch_size, 1), causing a broadcast shape mismatch during penalty application.

Impact

Any vLLM deployment between v0.18.0 and v0.19.1 (inclusive) configured with extract_hidden_states speculative decoding is affected. A single API request containing any penalty parameter immediately and permanently crashes the EngineCore process, resulting in complete loss of service availability.

Patches

Fixed in PR #38610, first included in vLLM v0.20.0. The fix slices the return value to sampled_token_ids[:, :1], ensuring the correct (batch_size, 1) shape regardless of the rejection sampler's output dimensions.

Workarounds

Upgrade to vLLM v0.20.0 or later.
If upgrading is not possible, avoid using extract_hidden_states as the speculative decoding method on affected versions.
Alternatively, reject or strip penalty parameters (repetition_penalty, frequency_penalty, presence_penalty) from incoming requests at an API gateway before they reach vLLM.

Severity

6.5 (Medium)


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

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.18.0"
            },
            {
              "fixed": "0.20.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-44223"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-131",
      "CWE-704"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-06T21:45:51Z",
    "nvd_published_at": "2026-05-12T20:16:43Z",
    "severity": "MODERATE"
  },
  "details": "### Summary\n\nThe `extract_hidden_states` speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a `RuntimeError` that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (`repetition_penalty`, `frequency_penalty`, or `presence_penalty`).\n\nA single request with a penalty parameter (e.g., `\"repetition_penalty\": 1.1`) is sufficient to crash the server. The crash is deterministic and immediate \u2014 no concurrency, race condition, or special workload is required.\n\n### Details\n\nIn vLLM v0.17.0, the `extract_hidden_states` proposer\u0027s `propose()` method returned `sampled_token_ids.unsqueeze(-1)`, producing a tensor of shape `(batch_size, 1)`.\n\nIn [PR #37013](https://github.com/vllm-project/vllm/pull/37013) (first released in v0.18.0), the KV connector interface was refactored out of `propose()`. The return type changed from `tuple[Tensor, KVConnectorOutput | None]` to `Tensor`, and the `.unsqueeze(-1)` call was removed along with the KV connector output:\n\n```python\n# Before (v0.17.0):\nreturn sampled_token_ids.unsqueeze(-1), kv_connector_output  # shape (batch_size, 1)\n\n# After (v0.18.0+):\nreturn sampled_token_ids  # shape (batch_size, 2) after first decode step\n```\n\nThe refactor missed that `sampled_token_ids` changed semantics between the first and subsequent decode steps. After the first decode step, the rejection sampler allocates its output as `(batch_size, max_spec_len + 1)`. With `num_speculative_tokens=1`, this produces shape `(batch_size, 2)` instead of the expected `(batch_size, 1)`, causing a broadcast shape mismatch during penalty application.\n\n### Impact\n\nAny vLLM deployment between v0.18.0 and v0.19.1 (inclusive) configured with `extract_hidden_states` speculative decoding is affected. A single API request containing any penalty parameter immediately and permanently crashes the EngineCore process, resulting in complete loss of service availability.\n\n### Patches\n\nFixed in [PR #38610](https://github.com/vllm-project/vllm/pull/38610), first included in vLLM v0.20.0. The fix slices the return value to `sampled_token_ids[:, :1]`, ensuring the correct `(batch_size, 1)` shape regardless of the rejection sampler\u0027s output dimensions.\n\n### Workarounds\n\n- Upgrade to vLLM v0.20.0 or later.\n- If upgrading is not possible, avoid using `extract_hidden_states` as the speculative decoding method on affected versions.\n- Alternatively, reject or strip penalty parameters (`repetition_penalty`, `frequency_penalty`, `presence_penalty`) from incoming requests at an API gateway before they reach vLLM.",
  "id": "GHSA-83vm-p52w-f9pw",
  "modified": "2026-06-08T19:52:35Z",
  "published": "2026-05-06T21:45:51Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44223"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/pull/38610"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-145.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/vllm-project/vllm"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "vLLM: extract_hidden_states speculative decoding crashes server on any request with penalty parameters"
}

PYSEC-2026-145

Vulnerability from pysec - Published: 2026-05-12 20:16 - Updated: 2026-05-20 09:19

Details

vLLM is an inference and serving engine for large language models (LLMs). From to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., "repetition_penalty": 1.1) is sufficient to crash the server. 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

Impacted products

Name	purl
vllm	pkg:pypi/vllm

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm",
        "purl": "pkg:pypi/vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.18.0"
            },
            {
              "fixed": "0.20.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.18.0",
        "0.18.1",
        "0.19.0",
        "0.19.1"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-44223",
    "GHSA-83vm-p52w-f9pw"
  ],
  "details": "vLLM is an inference and serving engine for large language models (LLMs). From  to before 0.20.0, the extract_hidden_states speculative decoding proposer in vLLM returns a tensor with an incorrect shape after the first decode step, causing a RuntimeError that crashes the EngineCore process. The crash is triggered when any request in the batch uses sampling penalty parameters (repetition_penalty, frequency_penalty, or presence_penalty). A single request with a penalty parameter (e.g., \"repetition_penalty\": 1.1) is sufficient to crash the server. This vulnerability is fixed in 0.20.0.",
  "id": "PYSEC-2026-145",
  "modified": "2026-05-20T09:19:21.596358Z",
  "published": "2026-05-12T20:16:43.293Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
    },
    {
      "type": "FIX",
      "url": "https://github.com/vllm-project/vllm/pull/38610"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ]
}

RHSA-2026:57380

Vulnerability from csaf_redhat - Published: 2026-08-20 07:55 - Updated: 2026-08-20 16:13

Summary

Red Hat Security Advisory: Red Hat AI Inference 3.4.4 (cpu)

Severity

Important

Notes

Topic: Red Hat AI Inference 3.4.4 (cpu) is now available.

Details: Red Hat AI Inference

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-2026-34753

A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system.

5.4 (Medium)


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

CWE-918 - Server-Side Request Forgery (SSRF)

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64		—	Vendor Fix fix Workaround

Threats

Impact Moderate

CVE-2026-34755

A flaw was found in vLLM, an inference and serving engine for large language models. A remote attacker can exploit a vulnerability in the VideoMediaIO.load_base64() method by sending a single API request containing a large number of comma-separated base64-encoded JPEG frames. This bypasses the intended frame count limit, causing the server to decode all frames into memory. This can lead to an Out-of-Memory (OOM) crash, resulting in a Denial of Service (DoS) for the affected system.

6.5 (Medium)


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

CWE-770 - Allocation of Resources Without Limits or Throttling

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-34756

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 'n' 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's event loop, causing the server to crash.

6.5 (Medium)


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

CWE-1284 - Improper Validation of Specified Quantity in Input

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

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

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-44223

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).

7.5 (High)


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

CWE-130 - Improper Handling of Length Parameter Inconsistency

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

References

39 references

URL	Category
https://access.redhat.com/errata/RHSA-2026:57380	self
https://access.redhat.com/security/cve/CVE-2026-34753	external
https://access.redhat.com/security/cve/CVE-2026-34755	external
https://access.redhat.com/security/cve/CVE-2026-34756	external
https://access.redhat.com/security/cve/CVE-2026-41523	external
https://access.redhat.com/security/cve/CVE-2026-44223	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-2026-34753	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455394	external
https://www.cve.org/CVERecord?id=CVE-2026-34753	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34753	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34755	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455403	external
https://www.cve.org/CVERecord?id=CVE-2026-34755	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34755	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34756	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455425	external
https://www.cve.org/CVERecord?id=CVE-2026-34756	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34756	external
https://github.com/vllm-project/vllm/commit/b111f…	external
https://github.com/vllm-project/vllm/pull/37952	external
https://github.com/vllm-project/vllm/security/adv…	external
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-2026-44223	self
https://bugzilla.redhat.com/show_bug.cgi?id=2476827	external
https://www.cve.org/CVERecord?id=CVE-2026-44223	external
https://nvd.nist.gov/vuln/detail/CVE-2026-44223	external
https://github.com/vllm-project/vllm/pull/38610	external
https://github.com/vllm-project/vllm/security/adv…	external

JSON

To clipboard

{
  "document": {
    "aggregate_severity": {
      "namespace": "https://access.redhat.com/security/updates/classification/",
      "text": "Important"
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        {
          "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 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_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": "2026-08-20T07:55:41+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57380",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57380"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_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 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-44223",
      "cwe": {
        "id": "CWE-130",
        "name": "Improper Handling of Length Parameter Inconsistency"
      },
      "discovery_date": "2026-05-12T21:02:06.713372+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2476827"
        }
      ],
      "notes": [
        {
          "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 AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-44223"
        },
        {
          "category": "external",
          "summary": "RHBZ#2476827",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2476827"
        },
        {
          "category": "external",
          "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",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
        }
      ],
      "release_date": "2026-05-12T19:58:40.862000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-20T07:55:41+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57380",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57380"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_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 3.4:registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed tensor shape in speculative decoding"
    }
  ]
}

RHSA-2026:57387

Vulnerability from csaf_redhat - Published: 2026-08-20 07:57 - Updated: 2026-08-20 16:13

Summary

Red Hat Security Advisory: Red Hat AI Inference 3.4.4 (spyre)

Severity

Important

Notes

Topic: Red Hat AI Inference 3.4.4 (spyre) is now available.

Details: Red Hat AI Inference

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-2026-34753

A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system.

5.4 (Medium)


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

CWE-918 - Server-Side Request Forgery (SSRF)

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Moderate

CVE-2026-34755

A flaw was found in vLLM, an inference and serving engine for large language models. A remote attacker can exploit a vulnerability in the VideoMediaIO.load_base64() method by sending a single API request containing a large number of comma-separated base64-encoded JPEG frames. This bypasses the intended frame count limit, causing the server to decode all frames into memory. This can lead to an Out-of-Memory (OOM) crash, resulting in a Denial of Service (DoS) for the affected system.

6.5 (Medium)


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

CWE-770 - Allocation of Resources Without Limits or Throttling

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-34756

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 'n' 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's event loop, causing the server to crash.

6.5 (Medium)


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

CWE-1284 - Improper Validation of Specified Quantity in Input

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-40192

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's 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.

7.5 (High)


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

CWE-409 - Improper Handling of Highly Compressed Data (Data Amplification)

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

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

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-42308

A flaw was found in Pillow, a Python imaging library. If a font advances for each glyph by an exceeding large amount, an integer overflow can occur when Pillow tracks the current position. This could lead to a denial of service (DoS) condition, making the application unavailable.

6.2 (Medium)


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

CWE-190 - Integer Overflow or Wraparound

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Moderate

CVE-2026-42309

A flaw was found in Pillow, a Python imaging library. A malicious actor could exploit this vulnerability by providing specially crafted nested lists as coordinates to image processing APIs within Pillow. This could lead to a heap buffer overflow, potentially causing a denial of service in applications using the library.

5.1 (Medium)


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

CWE-131 - Incorrect Calculation of Buffer Size

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix

Threats

Impact Moderate

CVE-2026-42310

A flaw was found in Pillow, a Python imaging library. A remote attacker could supply a specially crafted malicious PDF file, causing the application to hang indefinitely and consume 100% CPU. This vulnerability leads to a Denial of Service (DoS), making the application unresponsive.

4.0 (Medium)


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

CWE-835 - Loop with Unreachable Exit Condition ('Infinite Loop')

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix

Threats

Impact Moderate

CVE-2026-42311

A flaw was found in Pillow, a Python imaging library. An attacker could exploit this vulnerability by tricking a user into processing a specially crafted malicious PSD file. This could lead to memory corruption, potentially causing the application to crash or allowing for arbitrary code execution.

7.8 (High)


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

CWE-120 - Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-44223

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).

7.5 (High)


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

CWE-130 - Improper Handling of Length Parameter Inconsistency

Affected products

Fixed 3 products

Product	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64	—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le	—	Vendor Fix fix Workaround

Threats

Impact Important

References

77 references

URL	Category
https://access.redhat.com/errata/RHSA-2026:57387	self
https://access.redhat.com/security/cve/CVE-2026-34753	external
https://access.redhat.com/security/cve/CVE-2026-34755	external
https://access.redhat.com/security/cve/CVE-2026-34756	external
https://access.redhat.com/security/cve/CVE-2026-40192	external
https://access.redhat.com/security/cve/CVE-2026-41523	external
https://access.redhat.com/security/cve/CVE-2026-42308	external
https://access.redhat.com/security/cve/CVE-2026-42309	external
https://access.redhat.com/security/cve/CVE-2026-42310	external
https://access.redhat.com/security/cve/CVE-2026-42311	external
https://access.redhat.com/security/cve/CVE-2026-44223	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-2026-34753	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455394	external
https://www.cve.org/CVERecord?id=CVE-2026-34753	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34753	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34755	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455403	external
https://www.cve.org/CVERecord?id=CVE-2026-34755	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34755	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34756	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455425	external
https://www.cve.org/CVERecord?id=CVE-2026-34756	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34756	external
https://github.com/vllm-project/vllm/commit/b111f…	external
https://github.com/vllm-project/vllm/pull/37952	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-40192	self
https://bugzilla.redhat.com/show_bug.cgi?id=2458856	external
https://www.cve.org/CVERecord?id=CVE-2026-40192	external
https://nvd.nist.gov/vuln/detail/CVE-2026-40192	external
https://github.com/python-pillow/Pillow/commit/3c…	external
https://github.com/python-pillow/Pillow/pull/9521	external
https://github.com/python-pillow/Pillow/security/…	external
https://pillow.readthedocs.io/en/stable/releaseno…	external
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-2026-42308	self
https://bugzilla.redhat.com/show_bug.cgi?id=2468457	external
https://www.cve.org/CVERecord?id=CVE-2026-42308	external
https://nvd.nist.gov/vuln/detail/CVE-2026-42308	external
https://github.com/python-pillow/Pillow/releases/…	external
https://github.com/python-pillow/Pillow/security/…	external
https://access.redhat.com/security/cve/CVE-2026-42309	self
https://bugzilla.redhat.com/show_bug.cgi?id=2468458	external
https://www.cve.org/CVERecord?id=CVE-2026-42309	external
https://nvd.nist.gov/vuln/detail/CVE-2026-42309	external
https://github.com/python-pillow/Pillow/security/…	external
https://access.redhat.com/security/cve/CVE-2026-42310	self
https://bugzilla.redhat.com/show_bug.cgi?id=2468461	external
https://www.cve.org/CVERecord?id=CVE-2026-42310	external
https://nvd.nist.gov/vuln/detail/CVE-2026-42310	external
https://github.com/python-pillow/Pillow/commit/3b…	external
https://github.com/python-pillow/Pillow/pull/9519	external
https://github.com/python-pillow/Pillow/security/…	external
https://access.redhat.com/security/cve/CVE-2026-42311	self
https://bugzilla.redhat.com/show_bug.cgi?id=2468459	external
https://www.cve.org/CVERecord?id=CVE-2026-42311	external
https://nvd.nist.gov/vuln/detail/CVE-2026-42311	external
https://github.com/python-pillow/Pillow/commit/58…	external
https://github.com/python-pillow/Pillow/pull/9520	external
https://github.com/python-pillow/Pillow/security/…	external
https://access.redhat.com/security/cve/CVE-2026-44223	self
https://bugzilla.redhat.com/show_bug.cgi?id=2476827	external
https://www.cve.org/CVERecord?id=CVE-2026-44223	external
https://nvd.nist.gov/vuln/detail/CVE-2026-44223	external
https://github.com/vllm-project/vllm/pull/38610	external
https://github.com/vllm-project/vllm/security/adv…	external

JSON

To clipboard

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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x",
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le"
          ]
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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",
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          "products": [
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      "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"
        }
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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"
        },
        {
          "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": [
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          "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": "2026-08-20T07:57:23+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57387",
          "product_ids": [
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          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57387"
        },
        {
          "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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      "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"
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          "products": [
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64",
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      "threats": [
        {
          "category": "impact",
          "details": "Important"
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      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-42308",
      "cwe": {
        "id": "CWE-190",
        "name": "Integer Overflow or Wraparound"
      },
      "discovery_date": "2026-05-09T06:01:07.510498+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2468457"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. If a font advances for each glyph by an exceeding large amount, an integer overflow can occur when Pillow tracks the current position. This could lead to a denial of service (DoS) condition, making the application unavailable.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "Pillow: python: Pillow: Denial of Service via integer overflow in font processing",
          "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": [
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          "category": "self",
          "summary": "Canonical URL",
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          "url": "https://www.cve.org/CVERecord?id=CVE-2026-42308"
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        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-42308",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42308"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0",
          "url": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0"
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        {
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          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-wjx4-4jcj-g98j",
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          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57387",
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          "details": "To mitigate this issue, ensure that applications utilizing the Pillow library do not process untrusted or maliciously crafted font files. Additionally, consider running applications that process image data in a sandboxed environment to limit potential impact. Reloading or restarting affected services may be required for changes to take effect.",
          "product_ids": [
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            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 6.2,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
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          "category": "impact",
          "details": "Moderate"
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    },
    {
      "cve": "CVE-2026-42309",
      "cwe": {
        "id": "CWE-131",
        "name": "Incorrect Calculation of Buffer Size"
      },
      "discovery_date": "2026-05-09T06:01:11.401932+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2468458"
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        {
          "category": "description",
          "text": "A flaw was found in Pillow, a Python imaging library. A malicious actor could exploit this vulnerability by providing specially crafted nested lists as coordinates to image processing APIs within Pillow. This could lead to a heap buffer overflow, potentially causing a denial of service in applications using the library.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "Pillow: Pillow: Denial of Service via specially crafted coordinate input",
          "title": "Vulnerability summary"
        },
        {
          "category": "other",
          "text": "This Moderate impact vulnerability in the Pillow Python imaging library could lead to a denial of service. Applications processing untrusted image data that utilize specific Pillow APIs, such as `ImagePath.Path`, `ImageDraw.ImageDraw.polygon`, or `ImageDraw.ImageDraw.line`, with specially crafted nested list coordinates are susceptible to a heap buffer overflow.",
          "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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          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2468458"
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          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-42309",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-42309"
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        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-42309",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42309"
        },
        {
          "category": "external",
          "summary": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0",
          "url": "https://github.com/python-pillow/Pillow/releases/tag/12.2.0"
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          "summary": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-5xmw-vc9v-4wf2",
          "url": "https://github.com/python-pillow/Pillow/security/advisories/GHSA-5xmw-vc9v-4wf2"
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          "date": "2026-08-20T07:57:23+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57387",
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        {
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            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 5.1,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
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        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Moderate"
        }
      ],
      "title": "Pillow: Pillow: Denial of Service via specially crafted coordinate input"
    },
    {
      "cve": "CVE-2026-42310",
      "cwe": {
        "id": "CWE-835",
        "name": "Loop with Unreachable Exit Condition (\u0027Infinite Loop\u0027)"
      },
      "discovery_date": "2026-05-09T06:01:21.465052+00:00",
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        {
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          "text": "2468461"
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        {
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          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "Pillow: Pillow: Denial of Service via malicious PDF processing",
          "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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          "category": "external",
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          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-42310"
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          "summary": "https://github.com/python-pillow/Pillow/commit/3bf614e4b8615d0ce1d5039efaf6db447fe7c468",
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          "summary": "https://github.com/python-pillow/Pillow/pull/9519",
          "url": "https://github.com/python-pillow/Pillow/pull/9519"
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            "integrityImpact": "NONE",
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    {
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      "cwe": {
        "id": "CWE-120",
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          "title": "Vulnerability description"
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          "text": "Pillow: python-pillow: Pillow: Arbitrary code execution via malicious PSD file processing",
          "title": "Vulnerability summary"
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          "text": "This is an Important severity flaw in Pillow, a Python imaging library, that could lead to arbitrary code execution. The vulnerability requires a user to process a specially crafted malicious PSD file, which could result in memory corruption and allow an attacker to execute arbitrary code. Red Hat products that process untrusted image files using Pillow are at risk if they do not adequately sanitize input or operate in a sandboxed environment.",
          "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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            "scope": "UNCHANGED",
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          "title": "Vulnerability summary"
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          "title": "Statement"
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          "title": "CVSS score applicability"
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            "integrityImpact": "NONE",
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            "scope": "UNCHANGED",
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      "title": "vllm: vLLM: Denial of Service via malformed tensor shape in speculative decoding"
    }
  ]
}

RHSA-2026:57389

Vulnerability from csaf_redhat - Published: 2026-08-20 07:58 - Updated: 2026-08-20 16:13

Summary

Red Hat Security Advisory: Red Hat AI Inference 3.4.4 (cuda)

Severity

Important

Notes

Topic: Red Hat AI Inference 3.4.4 (cuda) is now available.

Details: Red Hat AI Inference

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-2026-34753

A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system.

5.4 (Medium)


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

CWE-918 - Server-Side Request Forgery (SSRF)

Affected products

Fixed 2 products

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64		—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64		—	Vendor Fix fix Workaround

Threats

Impact Moderate

CVE-2026-34755

A flaw was found in vLLM, an inference and serving engine for large language models. A remote attacker can exploit a vulnerability in the VideoMediaIO.load_base64() method by sending a single API request containing a large number of comma-separated base64-encoded JPEG frames. This bypasses the intended frame count limit, causing the server to decode all frames into memory. This can lead to an Out-of-Memory (OOM) crash, resulting in a Denial of Service (DoS) for the affected system.

6.5 (Medium)


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

CWE-770 - Allocation of Resources Without Limits or Throttling

Affected products

Fixed 2 products

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64		—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-34756

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 'n' 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's event loop, causing the server to crash.

6.5 (Medium)


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

CWE-1284 - Improper Validation of Specified Quantity in Input

Affected products

Fixed 2 products

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64		—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

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

Fixed 2 products

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64		—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-44223

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).

7.5 (High)


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

CWE-130 - Improper Handling of Length Parameter Inconsistency

Affected products

Fixed 2 products

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64		—	Vendor Fix fix Workaround
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

References

39 references

URL	Category
https://access.redhat.com/errata/RHSA-2026:57389	self
https://access.redhat.com/security/cve/CVE-2026-34753	external
https://access.redhat.com/security/cve/CVE-2026-34755	external
https://access.redhat.com/security/cve/CVE-2026-34756	external
https://access.redhat.com/security/cve/CVE-2026-41523	external
https://access.redhat.com/security/cve/CVE-2026-44223	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-2026-34753	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455394	external
https://www.cve.org/CVERecord?id=CVE-2026-34753	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34753	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34755	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455403	external
https://www.cve.org/CVERecord?id=CVE-2026-34755	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34755	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34756	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455425	external
https://www.cve.org/CVERecord?id=CVE-2026-34756	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34756	external
https://github.com/vllm-project/vllm/commit/b111f…	external
https://github.com/vllm-project/vllm/pull/37952	external
https://github.com/vllm-project/vllm/security/adv…	external
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-2026-44223	self
https://bugzilla.redhat.com/show_bug.cgi?id=2476827	external
https://www.cve.org/CVERecord?id=CVE-2026-44223	external
https://nvd.nist.gov/vuln/detail/CVE-2026-44223	external
https://github.com/vllm-project/vllm/pull/38610	external
https://github.com/vllm-project/vllm/security/adv…	external

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            "availabilityImpact": "LOW",
            "baseScore": 5.4,
            "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:L",
            "version": "3.1"
          },
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Moderate"
        }
      ],
      "title": "vllm: vLLM: Server-Side Request Forgery allows access to internal services via controlled batch input"
    },
    {
      "cve": "CVE-2026-34755",
      "cwe": {
        "id": "CWE-770",
        "name": "Allocation of Resources Without Limits or Throttling"
      },
      "discovery_date": "2026-04-06T16:02:21.718949+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2455403"
        }
      ],
      "notes": [
        {
          "category": "description",
          "text": "A flaw was found in vLLM, an inference and serving engine for large language models. A remote attacker can exploit a vulnerability in the VideoMediaIO.load_base64() method by sending a single API request containing a large number of comma-separated base64-encoded JPEG frames. This bypasses the intended frame count limit, causing the server to decode all frames into memory. This can lead to an Out-of-Memory (OOM) crash, resulting in a Denial of Service (DoS) for the affected system.",
          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vLLM: vLLM: Denial of Service due to excessive video frame processing",
          "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": [
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      },
      "references": [
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          "category": "self",
          "summary": "Canonical URL",
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        },
        {
          "category": "external",
          "summary": "RHBZ#2455403",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455403"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-34755",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-34755"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-34755",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34755"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p"
        }
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      "release_date": "2026-04-06T15:38:53.201000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-20T07:58:06+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57389",
          "product_ids": [
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
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          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57389"
        },
        {
          "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": 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",
            "version": "3.1"
          },
          "products": [
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "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"
        }
      ],
      "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": {
        "fixed": [
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          "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
        ]
      },
      "references": [
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          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-34756"
        },
        {
          "category": "external",
          "summary": "RHBZ#2455425",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455425"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-34756",
          "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"
        },
        {
          "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"
        }
      ],
      "release_date": "2026-04-06T15:40:03.448000+00:00",
      "remediations": [
        {
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          "date": "2026-08-20T07:58:06+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57389",
          "product_ids": [
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          ],
          "restart_required": {
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          "url": "https://access.redhat.com/errata/RHSA-2026:57389"
        },
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          "category": "workaround",
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          "product_ids": [
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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",
            "version": "3.1"
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
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      "threats": [
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          "category": "impact",
          "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"
      },
      "discovery_date": "2026-06-22T23:01:00.799590+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2491582"
        }
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      "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": [
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          "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
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          "category": "self",
          "summary": "Canonical URL",
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          "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"
        },
        {
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          "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": [
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          "category": "vendor_fix",
          "date": "2026-08-20T07:58:06+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57389",
          "product_ids": [
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57389"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64",
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      "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": [
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            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
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        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-44223",
      "cwe": {
        "id": "CWE-130",
        "name": "Improper Handling of Length Parameter Inconsistency"
      },
      "discovery_date": "2026-05-12T21:02:06.713372+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2476827"
        }
      ],
      "notes": [
        {
          "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": [
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          "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64"
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      },
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        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-44223"
        },
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          "category": "external",
          "summary": "RHBZ#2476827",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2476827"
        },
        {
          "category": "external",
          "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",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
        }
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      "release_date": "2026-05-12T19:58:40.862000+00:00",
      "remediations": [
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}

RHSA-2026:57390

Vulnerability from csaf_redhat - Published: 2026-08-20 07:58 - Updated: 2026-08-20 16:13

Summary

Red Hat Security Advisory: Red Hat AI Inference 3.4.4 (rocm)

Severity

Important

Notes

Topic: Red Hat AI Inference 3.4.4 (rocm) is now available.

Details: Red Hat AI Inference

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-2026-34753

A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system.

5.4 (Medium)


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

CWE-918 - Server-Side Request Forgery (SSRF)

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64		—	Vendor Fix fix Workaround

Threats

Impact Moderate

CVE-2026-34755

A flaw was found in vLLM, an inference and serving engine for large language models. A remote attacker can exploit a vulnerability in the VideoMediaIO.load_base64() method by sending a single API request containing a large number of comma-separated base64-encoded JPEG frames. This bypasses the intended frame count limit, causing the server to decode all frames into memory. This can lead to an Out-of-Memory (OOM) crash, resulting in a Denial of Service (DoS) for the affected system.

6.5 (Medium)


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

CWE-770 - Allocation of Resources Without Limits or Throttling

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-34756

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 'n' 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's event loop, causing the server to crash.

6.5 (Medium)


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

CWE-1284 - Improper Validation of Specified Quantity in Input

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

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

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

CVE-2026-44223

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).

7.5 (High)


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

CWE-130 - Improper Handling of Length Parameter Inconsistency

Affected products

Fixed 1 product

Product	Identifier	Version	Remediation
Unresolved product id: Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64		—	Vendor Fix fix Workaround

Threats

Impact Important

References

39 references

URL	Category
https://access.redhat.com/errata/RHSA-2026:57390	self
https://access.redhat.com/security/cve/CVE-2026-34753	external
https://access.redhat.com/security/cve/CVE-2026-34755	external
https://access.redhat.com/security/cve/CVE-2026-34756	external
https://access.redhat.com/security/cve/CVE-2026-41523	external
https://access.redhat.com/security/cve/CVE-2026-44223	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-2026-34753	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455394	external
https://www.cve.org/CVERecord?id=CVE-2026-34753	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34753	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34755	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455403	external
https://www.cve.org/CVERecord?id=CVE-2026-34755	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34755	external
https://github.com/vllm-project/vllm/security/adv…	external
https://access.redhat.com/security/cve/CVE-2026-34756	self
https://bugzilla.redhat.com/show_bug.cgi?id=2455425	external
https://www.cve.org/CVERecord?id=CVE-2026-34756	external
https://nvd.nist.gov/vuln/detail/CVE-2026-34756	external
https://github.com/vllm-project/vllm/commit/b111f…	external
https://github.com/vllm-project/vllm/pull/37952	external
https://github.com/vllm-project/vllm/security/adv…	external
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-2026-44223	self
https://bugzilla.redhat.com/show_bug.cgi?id=2476827	external
https://www.cve.org/CVERecord?id=CVE-2026-44223	external
https://nvd.nist.gov/vuln/detail/CVE-2026-44223	external
https://github.com/vllm-project/vllm/pull/38610	external
https://github.com/vllm-project/vllm/security/adv…	external

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          "title": "Vulnerability description"
        },
        {
          "category": "summary",
          "text": "vLLM: vLLM: Denial of Service due to excessive video frame processing",
          "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-34755"
        },
        {
          "category": "external",
          "summary": "RHBZ#2455403",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455403"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-34755",
          "url": "https://www.cve.org/CVERecord?id=CVE-2026-34755"
        },
        {
          "category": "external",
          "summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-34755",
          "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-34755"
        },
        {
          "category": "external",
          "summary": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p"
        }
      ],
      "release_date": "2026-04-06T15:38:53.201000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-20T07:58:42+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57390",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57390"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "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",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "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"
        }
      ],
      "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": {
        "fixed": [
          "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-34756"
        },
        {
          "category": "external",
          "summary": "RHBZ#2455425",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2455425"
        },
        {
          "category": "external",
          "summary": "https://www.cve.org/CVERecord?id=CVE-2026-34756",
          "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"
        },
        {
          "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"
        }
      ],
      "release_date": "2026-04-06T15:40:03.448000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-20T07:58:42+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57390",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57390"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "scores": [
        {
          "cvss_v3": {
            "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",
            "version": "3.1"
          },
          "products": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "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"
      },
      "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_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": "2026-08-20T07:58:42+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57390",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57390"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Arbitrary code execution via malicious HuggingFace model"
    },
    {
      "cve": "CVE-2026-44223",
      "cwe": {
        "id": "CWE-130",
        "name": "Improper Handling of Length Parameter Inconsistency"
      },
      "discovery_date": "2026-05-12T21:02:06.713372+00:00",
      "ids": [
        {
          "system_name": "Red Hat Bugzilla ID",
          "text": "2476827"
        }
      ],
      "notes": [
        {
          "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 AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
        ]
      },
      "references": [
        {
          "category": "self",
          "summary": "Canonical URL",
          "url": "https://access.redhat.com/security/cve/CVE-2026-44223"
        },
        {
          "category": "external",
          "summary": "RHBZ#2476827",
          "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2476827"
        },
        {
          "category": "external",
          "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",
          "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
        }
      ],
      "release_date": "2026-05-12T19:58:40.862000+00:00",
      "remediations": [
        {
          "category": "vendor_fix",
          "date": "2026-08-20T07:58:42+00:00",
          "details": "For more information visit https://access.redhat.com/errata/RHSA-2026:57390",
          "product_ids": [
            "Red Hat AI Inference Server 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ],
          "restart_required": {
            "category": "none"
          },
          "url": "https://access.redhat.com/errata/RHSA-2026:57390"
        },
        {
          "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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_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 3.4:registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64"
          ]
        }
      ],
      "threats": [
        {
          "category": "impact",
          "details": "Important"
        }
      ],
      "title": "vllm: vLLM: Denial of Service via malformed tensor shape in speculative decoding"
    }
  ]
}

WID-SEC-W-2026-1299

Vulnerability from csaf_certbund - Published: 2026-04-28 22:00 - Updated: 2026-05-12 22:00

Summary

vllm: Schwachstelle ermöglicht Denial of Service

Severity

Mittel

Notes

Das BSI ist als Anbieter für die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch dafür verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgfältig im Einzelfall zu prüfen.

Produktbeschreibung: Open Source vLLM ist eine Open-Source-Bibliothek für schnelle und effiziente Inferenz von Large Language Models (LLMs).

Angriff: Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm ausnutzen, um einen Denial of Service Angriff durchzuführen.

Betroffene Betriebssysteme: - Linux - UNIX

CVE-2026-44223

Affected products

Known affected 1 product

Product	Identifier	Version	Remediation
Open Source vllm <0.20.0 Open Source / vllm		<0.20.0

References

3 references

URL	Category
https://wid.cert-bund.de/.well-known/csaf/white/2…	self
https://wid.cert-bund.de/portal/wid/securityadvis…	self
https://github.com/vllm-project/vllm/security/adv…	external

JSON

To clipboard

{
  "document": {
    "aggregate_severity": {
      "text": "mittel"
    },
    "category": "csaf_base",
    "csaf_version": "2.0",
    "distribution": {
      "tlp": {
        "label": "WHITE",
        "url": "https://www.first.org/tlp/"
      }
    },
    "lang": "de-DE",
    "notes": [
      {
        "category": "legal_disclaimer",
        "text": "Das BSI ist als Anbieter f\u00fcr die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch daf\u00fcr verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgf\u00e4ltig im Einzelfall zu pr\u00fcfen."
      },
      {
        "category": "description",
        "text": "Open Source vLLM ist eine Open-Source-Bibliothek f\u00fcr schnelle und effiziente Inferenz von Large Language Models (LLMs).",
        "title": "Produktbeschreibung"
      },
      {
        "category": "summary",
        "text": "Ein entfernter, authentisierter Angreifer kann eine Schwachstelle in vllm ausnutzen, um einen Denial of Service Angriff durchzuf\u00fchren.",
        "title": "Angriff"
      },
      {
        "category": "general",
        "text": "- Linux\n- UNIX",
        "title": "Betroffene Betriebssysteme"
      }
    ],
    "publisher": {
      "category": "other",
      "contact_details": "csaf-provider@cert-bund.de",
      "name": "Bundesamt f\u00fcr Sicherheit in der Informationstechnik",
      "namespace": "https://www.bsi.bund.de"
    },
    "references": [
      {
        "category": "self",
        "summary": "WID-SEC-W-2026-1299 - CSAF Version",
        "url": "https://wid.cert-bund.de/.well-known/csaf/white/2026/wid-sec-w-2026-1299.json"
      },
      {
        "category": "self",
        "summary": "WID-SEC-2026-1299 - Portal Version",
        "url": "https://wid.cert-bund.de/portal/wid/securityadvisory?name=WID-SEC-2026-1299"
      },
      {
        "category": "external",
        "summary": "GitHub Security Advisory GHSA-83vm-p52w-f9pw vom 2026-04-28",
        "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw"
      }
    ],
    "source_lang": "en-US",
    "title": "vllm: Schwachstelle erm\u00f6glicht Denial of Service",
    "tracking": {
      "current_release_date": "2026-05-12T22:00:00.000+00:00",
      "generator": {
        "date": "2026-05-13T06:21:20.185+00:00",
        "engine": {
          "name": "BSI-WID",
          "version": "1.5.0"
        }
      },
      "id": "WID-SEC-W-2026-1299",
      "initial_release_date": "2026-04-28T22:00:00.000+00:00",
      "revision_history": [
        {
          "date": "2026-04-28T22:00:00.000+00:00",
          "number": "1",
          "summary": "Initiale Fassung"
        },
        {
          "date": "2026-05-12T22:00:00.000+00:00",
          "number": "2",
          "summary": "CVE erg\u00e4nzt"
        }
      ],
      "status": "final",
      "version": "2"
    }
  },
  "product_tree": {
    "branches": [
      {
        "branches": [
          {
            "branches": [
              {
                "category": "product_version_range",
                "name": "\u003c0.20.0",
                "product": {
                  "name": "Open Source vllm \u003c0.20.0",
                  "product_id": "T053386"
                }
              },
              {
                "category": "product_version",
                "name": "0.20.0",
                "product": {
                  "name": "Open Source vllm 0.20.0",
                  "product_id": "T053386-fixed",
                  "product_identification_helper": {
                    "cpe": "cpe:/a:vllm:vllm:0.20.0"
                  }
                }
              }
            ],
            "category": "product_name",
            "name": "vllm"
          }
        ],
        "category": "vendor",
        "name": "Open Source"
      }
    ]
  },
  "vulnerabilities": [
    {
      "cve": "CVE-2026-44223",
      "product_status": {
        "known_affected": [
          "T053386"
        ]
      },
      "release_date": "2026-04-28T22:00:00.000+00:00",
      "title": "CVE-2026-44223"
    }
  ]
}

CVE-2026-44223 (GCVE-0-2026-44223)

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CVE-2026-44223 (GCVE-0-2026-44223)

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