CVE-2025-1716: Mmaitre314 Picklescan RCE Vulnerability

CVE-2025-1716 Overview

CVE-2025-1716 is an insecure deserialization vulnerability in picklescan before version 0.0.21 that fails to treat pip as an unsafe global. This oversight allows attackers to craft malicious machine learning models that use Python's Pickle serialization format to execute arbitrary package installations via pip.main(). Because picklescan is specifically designed to detect unsafe Pickle objects in ML models, this bypass effectively negates its security purpose, allowing malicious models to pass security checks and appear safe when they could execute harmful code.

Critical Impact
Malicious ML models can bypass picklescan security scanning and execute arbitrary pip commands to install backdoored packages from PyPI or GitHub, potentially compromising ML pipelines and development environments.

Affected Products

mmaitre314 picklescan versions prior to 0.0.21
Applications and ML pipelines using vulnerable picklescan versions for model validation
Hugging Face Hub and similar model repositories relying on picklescan for security scanning

Discovery Timeline

February 26, 2025 - CVE-2025-1716 published to NVD
December 29, 2025 - Last updated in NVD database

Technical Details for CVE-2025-1716

Vulnerability Analysis

This vulnerability stems from an incomplete blocklist implementation in picklescan's security scanning mechanism. Picklescan is a security tool designed to detect potentially dangerous Pickle files, particularly in the context of machine learning model files that commonly use Pickle serialization. The tool maintains a list of "unsafe globals" - Python modules and functions that could be exploited when a Pickle file is deserialized.

The core issue is that picklescan's unsafe globals list did not include pip, Python's package installer module. This allows an attacker to create a Pickle payload that calls pip.main() with arbitrary arguments, effectively enabling remote code execution through package installation. When a model containing such a payload is scanned with the vulnerable version of picklescan, it passes all security checks despite containing dangerous code.

Root Cause

The root cause is classified under CWE-184: Incomplete List of Disallowed Inputs. The picklescan scanner maintained a blocklist of unsafe Python globals (such as bdb, pdb, and asyncio) but failed to include pip in this list. This incomplete allowlist/blocklist approach is a common security pattern failure where defenders must anticipate all possible attack vectors.

The pip module is particularly dangerous because:

It can download and execute arbitrary Python code from PyPI or URLs
It runs with the privileges of the current user
Package installation often includes post-install scripts that execute automatically

Attack Vector

An attacker can exploit this vulnerability by crafting a malicious ML model file that contains a specially constructed Pickle payload. The attack flow works as follows:

Attacker creates a Pickle payload that invokes pip.main(['install', 'malicious-package'])
The malicious package is hosted on PyPI, GitHub, or any accessible URL
The attacker distributes the model through ML model sharing platforms
When a victim downloads and scans the model with vulnerable picklescan, it passes security checks
Upon deserialization (model loading), the Pickle payload executes and installs the malicious package
The malicious package's code executes in the victim's environment

The following patch was applied to fix this vulnerability by adding pip to the unsafe globals list:

python

     "bdb": "*",
     "pdb": "*",
     "asyncio": "*",
+    "pip": "*",
 }
 
 #

Source: GitHub Commit

Detection Methods for CVE-2025-1716

Indicators of Compromise

Unexpected network connections to PyPI (pypi.org) or GitHub during model loading operations
Unusual pip installation activity in ML pipeline logs or environments
New or unexpected Python packages appearing in virtual environments after loading untrusted models
Process execution logs showing pip.main() calls originating from model deserialization

Detection Strategies

Monitor for pip subprocess execution during model loading operations in ML pipelines
Implement file integrity monitoring on Python site-packages directories
Deploy network monitoring rules to detect unexpected PyPI connections from ML infrastructure
Audit picklescan version in CI/CD pipelines and model validation workflows

Monitoring Recommendations

Enable verbose logging in ML model loading frameworks to capture deserialization events
Configure alerts for unexpected package installations in production ML environments
Review model provenance and maintain a trusted model registry with hash verification
Implement runtime monitoring for pip executions in containerized ML workloads

How to Mitigate CVE-2025-1716

Immediate Actions Required

Upgrade picklescan to version 0.0.21 or later immediately across all environments
Re-scan all previously validated ML models with the patched picklescan version
Quarantine any models that fail the updated security scan until further analysis
Review audit logs for evidence of malicious package installations from model loading

Patch Information

The vulnerability is fixed in picklescan version 0.0.21. The patch adds pip to the list of unsafe globals in src/picklescan/scanner.py, preventing Pickle payloads from invoking pip functionality. The fix is available via the GitHub commit 78ce704. For detailed information, refer to the GitHub Security Advisory GHSA-655q-fx9r-782v.

Workarounds

Isolate ML model loading in sandboxed environments with no network access
Implement network egress filtering to block pip and PyPI connections from model processing systems
Use alternative model formats (ONNX, SavedModel) that don't rely on Pickle serialization where possible
Deploy additional security scanning layers beyond picklescan for defense in depth

bash

# Upgrade picklescan to patched version
pip install --upgrade picklescan>=0.0.21

# Verify installed version
pip show picklescan | grep Version

# Re-scan existing models after upgrade
picklescan --path /path/to/models/