CNVD-2025-23289

Vulnerability from cnvd - Published: 2025-10-10
VLAI
Title
PyTorch存在未明漏洞(CNVD-2025-23289)
Description
PyTorch是PyTorch开源的一个Python包。 PyTorch存在安全漏洞,该漏洞源于文件torch/cuda/nccl.py中函数torch.cuda.nccl.reduce处理不当,攻击者可利用该漏洞导致拒绝服务。
Severity
低
Patch Name
PyTorch存在未明漏洞(CNVD-2025-23289)的补丁
Patch Description
PyTorch是PyTorch开源的一个Python包。 PyTorch存在安全漏洞,该漏洞源于文件torch/cuda/nccl.py中函数torch.cuda.nccl.reduce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://pytorch.org/

Reference
https://github.com/Divigroup-RAP/PYTORCH/commit/5827d2061dcb4acd05ac5f8e65d8693a481ba0f5
Impacted products
Name
Pytorch Pytorch 2.6.0+cu124
Show details on source website

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2025-4287",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2025-4287"
    }
  },
  "description": "PyTorch\u662fPyTorch\u5f00\u6e90\u7684\u4e00\u4e2aPython\u5305\u3002\n\nPyTorch\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u6587\u4ef6torch/cuda/nccl.py\u4e2d\u51fd\u6570torch.cuda.nccl.reduce\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://pytorch.org/",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2025-23289",
  "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\u6587\u4ef6torch/cuda/nccl.py\u4e2d\u51fd\u6570torch.cuda.nccl.reduce\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-2025-23289\uff09\u7684\u8865\u4e01",
  "products": {
    "product": "Pytorch Pytorch 2.6.0+cu124"
  },
  "referenceLink": "https://github.com/Divigroup-RAP/PYTORCH/commit/5827d2061dcb4acd05ac5f8e65d8693a481ba0f5",
  "serverity": "\u4f4e",
  "submitTime": "2025-05-14",
  "title": "PyTorch\u5b58\u5728\u672a\u660e\u6f0f\u6d1e\uff08CNVD-2025-23289\uff09"
}



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

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
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  • 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.

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