Vulnerability-Lookup

CNVD-2025-23278

Vulnerability from cnvd - Published: 2025-10-10

Title

PyTorch存在未明漏洞（CNVD-2025-23278）

Description

PyTorch是PyTorch开源的一个Python包。 PyTorch存在安全漏洞，该漏洞源于编译包含torch.Tensor.to_sparse和torch.Tensor.to_dense的模型时处理不当，攻击者可利用该漏洞导致拒绝服务。

Severity

高

Patch Name

PyTorch存在未明漏洞（CNVD-2025-23278）的补丁

Patch Description

PyTorch是PyTorch开源的一个Python包。 PyTorch存在安全漏洞，该漏洞源于编译包含torch.Tensor.to_sparse和torch.Tensor.to_dense的模型时处理不当，攻击者可利用该漏洞导致拒绝服务。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/pytorch/pytorch/releases

Reference

https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc

Impacted products

Name
Pytorch Pytorch v2.7.0

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2025-55560",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-55560"
    }
  },
  "description": "PyTorch\u662fPyTorch\u5f00\u6e90\u7684\u4e00\u4e2aPython\u5305\u3002\n\nPyTorch\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f16\u8bd1\u5305\u542btorch.Tensor.to_sparse\u548ctorch.Tensor.to_dense\u7684\u6a21\u578b\u65f6\u5904\u7406\u4e0d\u5f53\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://github.com/pytorch/pytorch/releases",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2025-23278",
  "openTime": "2025-10-10",
  "patchDescription": "PyTorch\u662fPyTorch\u5f00\u6e90\u7684\u4e00\u4e2aPython\u5305\u3002\r\n\r\nPyTorch\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f16\u8bd1\u5305\u542btorch.Tensor.to_sparse\u548ctorch.Tensor.to_dense\u7684\u6a21\u578b\u65f6\u5904\u7406\u4e0d\u5f53\uff0c\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "PyTorch\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2025-23278\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "Pytorch Pytorch v2.7.0"
  },
  "referenceLink": "https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc",
  "serverity": "\u9ad8",
  "submitTime": "2025-09-28",
  "title": "PyTorch\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2025-23278\uff09"
}

CVE-2025-55560 (GCVE-0-2025-55560)

Vulnerability from cvelistv5 – Published: 2025-09-25 00:00 – Updated: 2025-09-26 19:39

Summary

An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor.

Severity

7.5 (High)


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

SSVC

Exploitation: none Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-09-26 19:33 UTC

CWE

n/a
CWE-400 - Uncontrolled Resource Consumption

Assigner

mitre mitre root

References

3 references

URL	Tags
https://github.com/pytorch/pytorch/issues/151522
https://github.com/pytorch/pytorch/pull/151897
https://gist.github.com/shaoyuyoung/0e7d2a586297a…

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "cvssV3_1": {
              "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"
            }
          },
          {
            "other": {
              "content": {
                "id": "CVE-2025-55560",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "yes"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-09-26T19:33:24.696351Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "problemTypes": [
          {
            "descriptions": [
              {
                "cweId": "CWE-400",
                "description": "CWE-400 Uncontrolled Resource Consumption",
                "lang": "en",
                "type": "CWE"
              }
            ]
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-09-26T19:39:24.967Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "n/a",
          "vendor": "n/a",
          "versions": [
            {
              "status": "affected",
              "version": "n/a"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "An issue in pytorch v2.7.0 can lead to a Denial of Service (DoS) when a PyTorch model consists of torch.Tensor.to_sparse() and torch.Tensor.to_dense() and is compiled by Inductor."
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "description": "n/a",
              "lang": "en",
              "type": "text"
            }
          ]
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      ],
      "providerMetadata": {
        "dateUpdated": "2025-09-25T16:02:48.373Z",
        "orgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
        "shortName": "mitre"
      },
      "references": [
        {
          "url": "https://github.com/pytorch/pytorch/issues/151522"
        },
        {
          "url": "https://github.com/pytorch/pytorch/pull/151897"
        },
        {
          "url": "https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc"
        }
      ]
    }
  },
  "cveMetadata": {
    "assignerOrgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
    "assignerShortName": "mitre",
    "cveId": "CVE-2025-55560",
    "datePublished": "2025-09-25T00:00:00.000Z",
    "dateReserved": "2025-08-13T00:00:00.000Z",
    "dateUpdated": "2025-09-26T19:39:24.967Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

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

Detection rules are retrieved from Rulezet.

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

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.