PYSEC-2026-2288

Vulnerability from pysec - Published: 2026-04-07 06:16 - Updated: 2026-07-13 05:52
VLAI
Details

A vulnerability in the HuggingFace Transformers library, specifically in the Trainer class, allows for arbitrary code execution. The _load_rng_state() method in src/transformers/trainer.py at line 3059 calls torch.load() without the weights_only=True parameter. This issue affects all versions of the library supporting torch>=2.2 when used with PyTorch versions below 2.6, as the safe_globals() context manager provides no protection in these versions. An attacker can exploit this vulnerability by supplying a malicious checkpoint file, such as rng_state.pth, which can execute arbitrary code when loaded. The issue is resolved in version v5.0.0rc3.

Severity
7.8 (High)
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
Impacted products
Name purl
transformers pkg:pypi/transformers
Aliases
To clipboard 

{
  "affected": [
    {
      "ecosystem_specific": {},
      "package": {
        "ecosystem": "PyPI",
        "name": "transformers",
        "purl": "pkg:pypi/transformers"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "5.0.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.1",
        "2.0.0",
        "2.1.0",
        "2.1.1",
        "2.10.0",
        "2.11.0",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.3.0",
        "2.4.0",
        "2.4.1",
        "2.5.0",
        "2.5.1",
        "2.6.0",
        "2.7.0",
        "2.8.0",
        "2.9.0",
        "2.9.1",
        "3.0.0",
        "3.0.1",
        "3.0.2",
        "3.1.0",
        "3.2.0",
        "3.3.0",
        "3.3.1",
        "3.4.0",
        "3.5.0",
        "3.5.1",
        "4.0.0",
        "4.0.0rc1",
        "4.0.1",
        "4.1.0",
        "4.1.1",
        "4.10.0",
        "4.10.1",
        "4.10.2",
        "4.10.3",
        "4.11.0",
        "4.11.1",
        "4.11.2",
        "4.11.3",
        "4.12.0",
        "4.12.1",
        "4.12.2",
        "4.12.3",
        "4.12.4",
        "4.12.5",
        "4.13.0",
        "4.14.0",
        "4.14.1",
        "4.15.0",
        "4.16.0",
        "4.16.1",
        "4.16.2",
        "4.17.0",
        "4.18.0",
        "4.19.0",
        "4.19.1",
        "4.19.2",
        "4.19.3",
        "4.19.4",
        "4.2.0",
        "4.2.1",
        "4.2.2",
        "4.20.0",
        "4.20.1",
        "4.21.0",
        "4.21.1",
        "4.21.2",
        "4.21.3",
        "4.22.0",
        "4.22.1",
        "4.22.2",
        "4.23.0",
        "4.23.1",
        "4.24.0",
        "4.25.0",
        "4.25.1",
        "4.26.0",
        "4.26.1",
        "4.27.0",
        "4.27.1",
        "4.27.2",
        "4.27.3",
        "4.27.4",
        "4.28.0",
        "4.28.1",
        "4.29.0",
        "4.29.1",
        "4.29.2",
        "4.3.0",
        "4.3.0rc1",
        "4.3.1",
        "4.3.2",
        "4.3.3",
        "4.30.0",
        "4.30.1",
        "4.30.2",
        "4.31.0",
        "4.32.0",
        "4.32.1",
        "4.33.0",
        "4.33.1",
        "4.33.2",
        "4.33.3",
        "4.34.0",
        "4.34.1",
        "4.35.0",
        "4.35.1",
        "4.35.2",
        "4.36.0",
        "4.36.1",
        "4.36.2",
        "4.37.0",
        "4.37.1",
        "4.37.2",
        "4.38.0",
        "4.38.1",
        "4.38.2",
        "4.39.0",
        "4.39.1",
        "4.39.2",
        "4.39.3",
        "4.4.0",
        "4.4.1",
        "4.4.2",
        "4.40.0",
        "4.40.1",
        "4.40.2",
        "4.41.0",
        "4.41.1",
        "4.41.2",
        "4.42.0",
        "4.42.1",
        "4.42.2",
        "4.42.3",
        "4.42.4",
        "4.43.0",
        "4.43.1",
        "4.43.2",
        "4.43.3",
        "4.43.4",
        "4.44.0",
        "4.44.1",
        "4.44.2",
        "4.45.0",
        "4.45.1",
        "4.45.2",
        "4.46.0",
        "4.46.1",
        "4.46.2",
        "4.46.3",
        "4.47.0",
        "4.47.1",
        "4.48.0",
        "4.48.1",
        "4.48.2",
        "4.48.3",
        "4.49.0",
        "4.5.0",
        "4.5.1",
        "4.50.0",
        "4.50.1",
        "4.50.2",
        "4.50.3",
        "4.51.0",
        "4.51.1",
        "4.51.2",
        "4.51.3",
        "4.52.0",
        "4.52.1",
        "4.52.2",
        "4.52.3",
        "4.52.4",
        "4.53.0",
        "4.53.1",
        "4.53.2",
        "4.53.3",
        "4.54.0",
        "4.54.1",
        "4.55.0",
        "4.55.1",
        "4.55.2",
        "4.55.3",
        "4.55.4",
        "4.56.0",
        "4.56.1",
        "4.56.2",
        "4.57.0",
        "4.57.1",
        "4.57.2",
        "4.57.3",
        "4.57.4",
        "4.57.5",
        "4.57.6",
        "4.6.0",
        "4.6.1",
        "4.7.0",
        "4.8.0",
        "4.8.1",
        "4.8.2",
        "4.9.0",
        "4.9.1",
        "4.9.2",
        "5.0.0rc0",
        "5.0.0rc1",
        "5.0.0rc2",
        "5.0.0rc3"
      ]
    }
  ],
  "aliases": [
    "CVE-2026-1839",
    "GHSA-69w3-r845-3855"
  ],
  "details": "A vulnerability in the HuggingFace Transformers library, specifically in the `Trainer` class, allows for arbitrary code execution. The `_load_rng_state()` method in `src/transformers/trainer.py` at line 3059 calls `torch.load()` without the `weights_only=True` parameter. This issue affects all versions of the library supporting `torch\u003e=2.2` when used with PyTorch versions below 2.6, as the `safe_globals()` context manager provides no protection in these versions. An attacker can exploit this vulnerability by supplying a malicious checkpoint file, such as `rng_state.pth`, which can execute arbitrary code when loaded. The issue is resolved in version v5.0.0rc3.",
  "id": "PYSEC-2026-2288",
  "modified": "2026-07-13T05:52:14.613148Z",
  "published": "2026-04-07T06:16:41.490Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/huggingface/transformers/commit/03c8082ba4594c9b8d6fe190ca9bed0e5f8ca396"
    },
    {
      "type": "EVIDENCE",
      "url": "https://huntr.com/bounties/3c77bb97-e493-493d-9a88-c57f5c536485"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-69w3-r845-3855"
    }
  ],
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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.


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.