GHSA-6W4W-5W54-RJVR

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 idlelib.autocomplete.AutoComplete.get_entity
Details

Summary

Using idlelib.autocomplete.AutoComplete.get_entity, 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 idlelib.autocomplete.AutoComplete.get_entity 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 EvilIdlelibAutocompleteGetEntity:
    def __reduce__(self):
        from idlelib.autocomplete import AutoComplete
        return AutoComplete().get_entity, ("__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

{
  "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": "2025-08-26T18:37:10Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Summary\n\nUsing idlelib.autocomplete.AutoComplete.get_entity, 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 idlelib.autocomplete.AutoComplete.get_entity 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 EvilIdlelibAutocompleteGetEntity:\n    def __reduce__(self):\n        from idlelib.autocomplete import AutoComplete\n        return AutoComplete().get_entity, (\"__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-6w4w-5w54-rjvr",
  "modified": "2025-08-26T18:37:10Z",
  "published": "2025-08-26T18:37:10Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-6w4w-5w54-rjvr"
    },
    {
      "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 idlelib.autocomplete.AutoComplete.get_entity"
}


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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.

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Detection rules are retrieved from Rulezet.

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