Vulnerability-Lookup

BDU:2025-12835

Vulnerability from fstec - Published: 16.04.2025

Title

Уязвимость метода torch.Tensor.random_() фреймворка машинного обучения PyTorch, позволяющая нарушителю вызвать отказ в обслуживании

Description

Уязвимость метода torch.Tensor.random_() фреймворка машинного обучения PyTorch связана с недостаточной обработкой исключений. Эксплуатация уязвимости может позволить нарушителю, действующему удаленно, вызвать отказ в обслуживании

Severity


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

Vendor

Сообщество свободного программного обеспечения

Software Name

PyTorch

Software Version

до 2.7.0 включительно (PyTorch)

Possible Mitigations

Использование рекомендаций: https://github.com/pytorch/pytorch/commit/f9dc20c7a3409865ff72c02575068edc1797473f https://github.com/ROCm/pytorch/commit/93a5909263a0ebf38f17c7046170c09e073459b3 https://github.com/pytorch/pytorch/commit/2999c45deae36a1eff8a6947f1b127212236cb83

Reference

https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc https://github.com/pytorch/pytorch/issues/151432 https://github.com/pytorch/pytorch/pull/154645

CWE

CWE-248

JSON

To clipboard

{
  "CVSS 2.0": "AV:N/AC:L/Au:N/C:N/I:N/A:C",
  "CVSS 3.0": "AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
  "CVSS 4.0": null,
  "remediation_\u0418\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440": null,
  "remediation_\u041d\u0430\u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u0435": null,
  "\u0412\u0435\u043d\u0434\u043e\u0440 \u041f\u041e": "\u0421\u043e\u043e\u0431\u0449\u0435\u0441\u0442\u0432\u043e \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0433\u043e \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u044f",
  "\u0412\u0435\u0440\u0441\u0438\u044f \u041f\u041e": "\u0434\u043e 2.7.0 \u0432\u043a\u043b\u044e\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e (PyTorch)",
  "\u0412\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u043c\u0435\u0440\u044b \u043f\u043e \u0443\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u044e": "\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0439:\nhttps://github.com/pytorch/pytorch/commit/f9dc20c7a3409865ff72c02575068edc1797473f\nhttps://github.com/ROCm/pytorch/commit/93a5909263a0ebf38f17c7046170c09e073459b3\nhttps://github.com/pytorch/pytorch/commit/2999c45deae36a1eff8a6947f1b127212236cb83",
  "\u0414\u0430\u0442\u0430 \u0432\u044b\u044f\u0432\u043b\u0435\u043d\u0438\u044f": "16.04.2025",
  "\u0414\u0430\u0442\u0430 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0435\u0433\u043e \u043e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u044f": "14.10.2025",
  "\u0414\u0430\u0442\u0430 \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u0438": "14.10.2025",
  "\u0418\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440": "BDU:2025-12835",
  "\u0418\u0434\u0435\u043d\u0442\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u044b \u0434\u0440\u0443\u0433\u0438\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0439 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "CVE-2025-55553",
  "\u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f \u043e\u0431 \u0443\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u0438": "\u0423\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u044c \u0443\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0430",
  "\u041a\u043b\u0430\u0441\u0441 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u0423\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u044c \u043a\u043e\u0434\u0430",
  "\u041d\u0430\u0437\u0432\u0430\u043d\u0438\u0435 \u041f\u041e": "PyTorch",
  "\u041d\u0430\u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u041e\u0421 \u0438 \u0442\u0438\u043f \u0430\u043f\u043f\u0430\u0440\u0430\u0442\u043d\u043e\u0439 \u043f\u043b\u0430\u0442\u0444\u043e\u0440\u043c\u044b": null,
  "\u041d\u0430\u0438\u043c\u0435\u043d\u043e\u0432\u0430\u043d\u0438\u0435 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u0423\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u044c \u043c\u0435\u0442\u043e\u0434\u0430 torch.Tensor.random_() \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f PyTorch, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0430\u044f \u043d\u0430\u0440\u0443\u0448\u0438\u0442\u0435\u043b\u044e \u0432\u044b\u0437\u0432\u0430\u0442\u044c \u043e\u0442\u043a\u0430\u0437 \u0432 \u043e\u0431\u0441\u043b\u0443\u0436\u0438\u0432\u0430\u043d\u0438\u0438",
  "\u041d\u0430\u043b\u0438\u0447\u0438\u0435 \u044d\u043a\u0441\u043f\u043b\u043e\u0439\u0442\u0430": "\u0414\u0430\u043d\u043d\u044b\u0435 \u0443\u0442\u043e\u0447\u043d\u044f\u044e\u0442\u0441\u044f",
  "\u041e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043e\u0448\u0438\u0431\u043a\u0438 CWE": "\u041d\u0435 \u043f\u0435\u0440\u0435\u0445\u0432\u0430\u0447\u0435\u043d\u043d\u043e\u0435 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 (CWE-248)",
  "\u041e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u0423\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u044c \u043c\u0435\u0442\u043e\u0434\u0430 torch.Tensor.random_() \u0444\u0440\u0435\u0439\u043c\u0432\u043e\u0440\u043a\u0430 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f PyTorch \u0441\u0432\u044f\u0437\u0430\u043d\u0430 \u0441 \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e\u0439 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u043e\u0439 \u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0439. \u042d\u043a\u0441\u043f\u043b\u0443\u0430\u0442\u0430\u0446\u0438\u044f \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438 \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u0437\u0432\u043e\u043b\u0438\u0442\u044c \u043d\u0430\u0440\u0443\u0448\u0438\u0442\u0435\u043b\u044e, \u0434\u0435\u0439\u0441\u0442\u0432\u0443\u044e\u0449\u0435\u043c\u0443 \u0443\u0434\u0430\u043b\u0435\u043d\u043d\u043e, \u0432\u044b\u0437\u0432\u0430\u0442\u044c \u043e\u0442\u043a\u0430\u0437 \u0432 \u043e\u0431\u0441\u043b\u0443\u0436\u0438\u0432\u0430\u043d\u0438\u0438",
  "\u041f\u043e\u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u044f \u044d\u043a\u0441\u043f\u043b\u0443\u0430\u0442\u0430\u0446\u0438\u0438 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": null,
  "\u041f\u0440\u043e\u0447\u0430\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f": null,
  "\u0421\u0432\u044f\u0437\u044c \u0441 \u0438\u043d\u0446\u0438\u0434\u0435\u043d\u0442\u0430\u043c\u0438 \u0418\u0411": "\u0414\u0430\u043d\u043d\u044b\u0435 \u0443\u0442\u043e\u0447\u043d\u044f\u044e\u0442\u0441\u044f",
  "\u0421\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d\u0430",
  "\u0421\u043f\u043e\u0441\u043e\u0431 \u0443\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u044f": "\u041e\u0431\u043d\u043e\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u043e\u0433\u043e \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0435\u043d\u0438\u044f",
  "\u0421\u043f\u043e\u0441\u043e\u0431 \u044d\u043a\u0441\u043f\u043b\u0443\u0430\u0442\u0430\u0446\u0438\u0438": "\u041c\u0430\u043d\u0438\u043f\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0440\u043e\u043a\u0430\u043c\u0438 \u0438 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u0435\u043c",
  "\u0421\u0441\u044b\u043b\u043a\u0438 \u043d\u0430 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438": "https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc \nhttps://github.com/pytorch/pytorch/issues/151432 \nhttps://github.com/pytorch/pytorch/pull/154645",
  "\u0421\u0442\u0430\u0442\u0443\u0441 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u041f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0430 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0435\u043c",
  "\u0422\u0438\u043f \u041f\u041e": "\u041f\u041e \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0418\u0418",
  "\u0422\u0438\u043f \u043e\u0448\u0438\u0431\u043a\u0438 CWE": "CWE-248",
  "\u0423\u0440\u043e\u0432\u0435\u043d\u044c \u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 \u0443\u044f\u0437\u0432\u0438\u043c\u043e\u0441\u0442\u0438": "\u0412\u044b\u0441\u043e\u043a\u0438\u0439 \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 (\u0431\u0430\u0437\u043e\u0432\u0430\u044f \u043e\u0446\u0435\u043d\u043a\u0430 CVSS 2.0 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 7,8)\n\u0412\u044b\u0441\u043e\u043a\u0438\u0439 \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u043e\u043f\u0430\u0441\u043d\u043e\u0441\u0442\u0438 (\u0431\u0430\u0437\u043e\u0432\u0430\u044f \u043e\u0446\u0435\u043d\u043a\u0430 CVSS 3.1 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 7,5)"
}

CVE-2025-55553 (GCVE-0-2025-55553)

Vulnerability from cvelistv5 – Published: 2025-09-25 00:00 – Updated: 2025-09-29 17:30

Summary

A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS).

Severity

7.5 (High)


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

SSVC

Exploitation: poc Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-09-29 17:28 UTC

CWE

n/a
CWE-248 - Uncaught Exception

Assigner

mitre mitre root

References

3 references

URL	Tags
https://github.com/pytorch/pytorch/issues/151432
https://github.com/pytorch/pytorch/pull/154645
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-2025-55553",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "yes"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-09-29T17:28:46.243940Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "problemTypes": [
          {
            "descriptions": [
              {
                "cweId": "CWE-248",
                "description": "CWE-248 Uncaught Exception",
                "lang": "en",
                "type": "CWE"
              }
            ]
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-09-29T17:30:11.735Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "references": [
          {
            "tags": [
              "exploit"
            ],
            "url": "https://github.com/pytorch/pytorch/issues/151432"
          }
        ],
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "n/a",
          "vendor": "n/a",
          "versions": [
            {
              "status": "affected",
              "version": "n/a"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "A syntax error in the component proxy_tensor.py of pytorch v2.7.0 allows attackers to cause a Denial of Service (DoS)."
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "description": "n/a",
              "lang": "en",
              "type": "text"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2025-09-25T15:11:38.959Z",
        "orgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
        "shortName": "mitre"
      },
      "references": [
        {
          "url": "https://github.com/pytorch/pytorch/issues/151432"
        },
        {
          "url": "https://github.com/pytorch/pytorch/pull/154645"
        },
        {
          "url": "https://gist.github.com/shaoyuyoung/0e7d2a586297ae9c8ed14d8706749efc"
        }
      ]
    }
  },
  "cveMetadata": {
    "assignerOrgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
    "assignerShortName": "mitre",
    "cveId": "CVE-2025-55553",
    "datePublished": "2025-09-25T00:00:00.000Z",
    "dateReserved": "2025-08-13T00:00:00.000Z",
    "dateUpdated": "2025-09-29T17:30:11.735Z",
    "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.