GHSA-F4HP-RMR7-R7V8

Vulnerability from github – Published: 2025-03-31 15:30 – Updated: 2026-06-09 21:56
VLAI
Summary
PyTorch is Vulnerable to Memory Consumption through pad_packed_sequence Function
Details

A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.

Severity
5.3 (Medium)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L
4.8 (Medium)
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "torch"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "2.6.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2025-2998"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-119"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-06-09T21:56:31Z",
    "nvd_published_at": "2025-03-31T14:15:20Z",
    "severity": "MODERATE"
  },
  "details": "A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.",
  "id": "GHSA-f4hp-rmr7-r7v8",
  "modified": "2026-06-09T21:56:31Z",
  "published": "2025-03-31T15:30:48Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-2998"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pytorch/pytorch/issues/149622"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pytorch/pytorch/issues/149622#issue-2935495265"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pytorch/pytorch/commit/494518046816d29099b7d056a74ffa5c244fdcdd"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/torch/PYSEC-2025-192.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/pytorch/pytorch"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.302047"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.302047"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.524151"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "PyTorch is Vulnerable to Memory Consumption through pad_packed_sequence Function"
}


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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.
  • 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.