Vulnerability-Lookup

GHSA-4VMG-RW8F-92F9

Vulnerability from github – Published: 2025-03-20 12:32 – Updated: 2025-04-02 13:31

Withdrawn 2025-04-02

Summary

Withdrawn Advisory: PyTorch deserialization vulnerability

Details

Withdrawn Advisory

This advisory has been withdrawn because it describes known functionality of PyTorch. This link is maintained to preserve external references.

Original Description

A deserialization vulnerability exists in the Pytorch RPC framework (torch.distributed.rpc) in pytorch/pytorch versions <=2.3.1. The vulnerability arises from the lack of security verification during the deserialization process of PythonUDF objects in pytorch/torch/distributed/rpc/internal.py. This flaw allows an attacker to execute arbitrary code remotely by sending a malicious serialized PythonUDF object, leading to remote code execution (RCE) on the master node.

Severity

9.8 (Critical)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "torch"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2024-7804"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2025-03-21T21:55:51Z",
    "nvd_published_at": "2025-03-20T10:15:37Z",
    "severity": "CRITICAL"
  },
  "details": "## Withdrawn Advisory\nThis advisory has been withdrawn because it describes known functionality of PyTorch. This link is maintained to preserve external references.\n\n## Original Description\nA deserialization vulnerability exists in the Pytorch RPC framework (torch.distributed.rpc) in pytorch/pytorch versions \u003c=2.3.1. The vulnerability arises from the lack of security verification during the deserialization process of PythonUDF objects in pytorch/torch/distributed/rpc/internal.py. This flaw allows an attacker to execute arbitrary code remotely by sending a malicious serialized PythonUDF object, leading to remote code execution (RCE) on the master node.",
  "id": "GHSA-4vmg-rw8f-92f9",
  "modified": "2025-04-02T13:31:15Z",
  "published": "2025-03-20T12:32:46Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-7804"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/pytorch/pytorch"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pytorch/pytorch/blob/27a14405d3b996d572ba18339410e29ec005c775/torch/distributed/rpc/internal.py#L162"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/0e870eeb-f924-4054-8fac-d926b1fb7259"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Withdrawn Advisory: PyTorch deserialization vulnerability",
  "withdrawn": "2025-04-02T13:31:15Z"
}

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.

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