GHSA-CJ3C-V495-4XQH

Vulnerability from github – Published: 2025-08-26 18:37 – Updated: 2025-08-26 18:37
VLAI
Summary
Picklescan has a missing detection when calling built-in python code.InteractiveInterpreter
Details

Summary

Using code.InteractiveInterpreter.runcode, which is a built-in python library function to execute remote pickle file.

Details

The attack payload executes in the following steps:

First, the attacker craft the payload by calling to code.InteractiveInterpreter.runcode function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.

PoC

class EvilCodeRuncode:
    def __reduce__(self):
        from code import InteractiveInterpreter
        # InteractiveInterpreter().runcode(cmd) -> exec(cmd)
        return InteractiveInterpreter().runcode, ("__import__('os').system('whoami')",)

Impact

Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.

Corresponding

https://github.com/FredericDT https://github.com/Qhaoduoyu

Show details on source website
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "picklescan"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "0.0.29"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": true,
    "github_reviewed_at": "2025-08-26T18:37:50Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Summary\n\nUsing code.InteractiveInterpreter.runcode, which is a built-in python library function to execute remote pickle file.\n\n### Details\n\nThe attack payload executes in the following steps:\n\nFirst, the attacker craft the payload by calling to code.InteractiveInterpreter.runcode function in reduce method\nThen when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn\u0027t dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution.\n\n### PoC\n\n```\nclass EvilCodeRuncode:\n    def __reduce__(self):\n        from code import InteractiveInterpreter\n        # InteractiveInterpreter().runcode(cmd) -\u003e exec(cmd)\n        return InteractiveInterpreter().runcode, (\"__import__(\u0027os\u0027).system(\u0027whoami\u0027)\",)\n```\n\n### Impact\n\nWho is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models.\nWhat is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded.\nSupply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects.\n\n### Corresponding\n\nhttps://github.com/FredericDT\nhttps://github.com/Qhaoduoyu",
  "id": "GHSA-cj3c-v495-4xqh",
  "modified": "2025-08-26T18:37:50Z",
  "published": "2025-08-26T18:37:50Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-cj3c-v495-4xqh"
    },
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/commit/aecd11be98702caa9ba9b12189d91ad596a36114"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/mmaitre314/picklescan"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [],
  "summary": "Picklescan has a missing detection when calling built-in python code.InteractiveInterpreter"
}


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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.

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Detection rules are retrieved from Rulezet.