ubuntu-cve-2026-24747
Vulnerability from osv_ubuntu
Published
2026-01-27 22:15
Modified
2026-06-30 16:19
Summary
Details

PyTorch is a Python package that provides tensor computation. Prior to version 2.10.0, a vulnerability in PyTorch's weights_only unpickler allows an attacker to craft a malicious checkpoint file (.pth) that, when loaded with torch.load(..., weights_only=True), can corrupt memory and potentially lead to arbitrary code execution. Version 2.10.0 fixes the issue.

Severity
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
8.8 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
N/A (UNKNOWN)
medium
References
To clipboard 

{
  "affected": [
    {
      "ecosystem_specific": {
        "binaries": [
          {
            "binary_name": "libtorch-test",
            "binary_version": "1.8.1-4"
          },
          {
            "binary_name": "libtorch1.8",
            "binary_version": "1.8.1-4"
          },
          {
            "binary_name": "python3-torch",
            "binary_version": "1.8.1-4"
          }
        ]
      },
      "package": {
        "ecosystem": "Ubuntu:22.04:LTS",
        "name": "pytorch",
        "purl": "pkg:deb/ubuntu/pytorch@1.8.1-4?arch=source\u0026distro=jammy"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "1.7.1-7",
        "1.8.1-4"
      ]
    },
    {
      "ecosystem_specific": {
        "binaries": [
          {
            "binary_name": "libtorch-test",
            "binary_version": "2.6.0+dfsg-7"
          },
          {
            "binary_name": "libtorch2.6",
            "binary_version": "2.6.0+dfsg-7"
          },
          {
            "binary_name": "python3-torch",
            "binary_version": "2.6.0+dfsg-7"
          }
        ]
      },
      "package": {
        "ecosystem": "Ubuntu:25.10",
        "name": "pytorch",
        "purl": "pkg:deb/ubuntu/pytorch@2.6.0+dfsg-7?arch=source\u0026distro=questing"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0+dfsg-5build1",
        "2.6.0+dfsg-7"
      ]
    },
    {
      "ecosystem_specific": {
        "binaries": [
          {
            "binary_name": "libtorch-test",
            "binary_version": "2.9.1+dfsg-1~exp1ubuntu2"
          },
          {
            "binary_name": "libtorch2.9",
            "binary_version": "2.9.1+dfsg-1~exp1ubuntu2"
          },
          {
            "binary_name": "python3-torch",
            "binary_version": "2.9.1+dfsg-1~exp1ubuntu2"
          }
        ]
      },
      "package": {
        "ecosystem": "Ubuntu:26.04:LTS",
        "name": "pytorch",
        "purl": "pkg:deb/ubuntu/pytorch@2.9.1+dfsg-1~exp1ubuntu2?arch=source\u0026distro=resolute"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0+dfsg-7",
        "2.6.0+dfsg-9",
        "2.9.1+dfsg-1~exp1ubuntu2"
      ]
    }
  ],
  "aliases": [],
  "details": "PyTorch is a Python package that provides tensor computation. Prior to version 2.10.0, a vulnerability in PyTorch\u0027s `weights_only` unpickler allows an attacker to craft a malicious checkpoint file (`.pth`) that, when loaded with `torch.load(..., weights_only=True)`, can corrupt memory and potentially lead to arbitrary code execution. Version 2.10.0 fixes the issue.",
  "id": "UBUNTU-CVE-2026-24747",
  "modified": "2026-06-30T16:19:38Z",
  "published": "2026-01-27T22:15:00Z",
  "references": [
    {
      "type": "REPORT",
      "url": "https://ubuntu.com/security/CVE-2026-24747"
    },
    {
      "type": "REPORT",
      "url": "https://www.cve.org/CVERecord?id=CVE-2026-24747"
    },
    {
      "type": "REPORT",
      "url": "https://github.com/pytorch/pytorch/163122/commit/954dc5183ee9205cbe79876ad05dd2d9ae752139"
    },
    {
      "type": "REPORT",
      "url": "https://github.com/pytorch/pytorch/issues/163105"
    },
    {
      "type": "REPORT",
      "url": "https://github.com/pytorch/pytorch/releases/tag/v2.10.0"
    },
    {
      "type": "REPORT",
      "url": "https://github.com/pytorch/pytorch/security/advisories/GHSA-63cw-57p8-fm3p"
    }
  ],
  "related": [],
  "schema_version": "1.7.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "medium",
      "type": "Ubuntu"
    }
  ],
  "upstream": [
    "CVE-2026-24747"
  ]
}



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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

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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.