CVE-2025-1889: Picklescan Information Disclosure Flaw

CVE-2025-1889 Overview

CVE-2025-1889 is a security bypass vulnerability in picklescan, a Python security tool designed to detect malicious pickle files in machine learning models. The vulnerability exists because picklescan versions prior to 0.0.22 only consider standard pickle file extensions when determining the scope of its vulnerability scan. An attacker could craft a malicious model that includes a pickle file with a non-standard file extension, effectively bypassing picklescan's security checks. This allows malicious pickle payloads to appear safe while potentially containing harmful code.

Critical Impact
Attackers can bypass ML model security scanning by using non-standard file extensions for malicious pickle files, potentially leading to arbitrary code execution when the model is loaded.

Affected Products

mmaitre314 picklescan versions before 0.0.22

Discovery Timeline

2025-03-03 - CVE-2025-1889 published to NVD
2025-12-29 - Last updated in NVD database

Technical Details for CVE-2025-1889

Vulnerability Analysis

This vulnerability is classified under CWE-646 (Reliance on File Name or Extension of Externally-Supplied File). The root cause lies in picklescan's incomplete approach to identifying pickle files for security analysis. Python's pickle serialization format is inherently dangerous because it can execute arbitrary code during deserialization. Security tools like picklescan are designed to scan machine learning models and detect malicious pickle content before it can be executed.

However, picklescan's implementation relied solely on file extensions to determine which files to scan. This creates a security gap because pickle files can technically use any file extension. The Python pickle module will happily deserialize data regardless of the file's extension, meaning an attacker can rename a malicious pickle file to use an obscure or custom extension (such as .data, .bin, .model, or any arbitrary string) to evade detection.

Root Cause

The vulnerability stems from an allowlist-based approach to file identification. Picklescan maintained a list of "known" pickle file extensions and only scanned files matching those extensions. This design fundamentally misunderstands how attackers operate—they will simply use unlisted extensions to bypass the security control. The proper approach is to either analyze file content headers or treat all binary files as potentially dangerous, rather than relying on easily-spoofed file metadata.

Attack Vector

The attack vector is network-based, requiring user interaction. An attacker would create a malicious machine learning model package containing embedded pickle files. By naming these pickle files with non-standard extensions, the attacker ensures picklescan does not flag them during security scanning. When a victim downloads and loads the seemingly "safe" model, the malicious pickle code executes, potentially compromising the system.

The attack scenario typically involves:

Attacker creates a malicious pickle payload designed to execute arbitrary code
Attacker renames the pickle file to use a non-standard extension (e.g., weights.bin instead of weights.pkl)
Attacker packages this into a machine learning model and distributes it via model repositories
Victim runs picklescan against the model, which reports no threats
Victim loads the model in their Python environment, triggering code execution

Detection Methods for CVE-2025-1889

Indicators of Compromise

Presence of binary files with unusual extensions in machine learning model packages that contain pickle serialization magic bytes (\\x80\\x03 or \\x80\\x04 at file start)
Model packages containing executable code embedded in non-standard file formats
Unexpected network connections or process spawning after loading ML models

Detection Strategies

Implement content-based detection that examines file headers rather than extensions to identify pickle format
Monitor Python environments for suspicious pickle.load() or torch.load() calls on files with non-standard extensions
Scan all binary files within model archives regardless of extension using updated picklescan version 0.0.22 or later
Review model provenance and verify models come from trusted sources

Monitoring Recommendations

Enable logging for all pickle deserialization operations in production environments
Implement sandbox environments for initial model loading and testing
Monitor for anomalous file access patterns when loading machine learning models
Alert on execution of child processes originating from Python ML frameworks

How to Mitigate CVE-2025-1889

Immediate Actions Required

Upgrade picklescan to version 0.0.22 or later immediately
Re-scan all previously approved models with the updated version of picklescan
Quarantine and review any models that were scanned with vulnerable versions of picklescan
Consider implementing additional defense-in-depth measures for ML model loading

Patch Information

The vulnerability has been addressed in picklescan version 0.0.22. The fix expands the scope of file scanning beyond standard pickle extensions to properly detect malicious pickle content regardless of file naming. Users should upgrade to version 0.0.22 or later to receive the security fix.

For additional details, see the GitHub Security Advisory and Sonatype Security Advisory.

Workarounds

Manually inspect all binary files in model packages using file command or hex dump to identify pickle magic bytes
Implement additional scanning layers that analyze file content headers rather than extensions
Use safe loading alternatives where available (e.g., safetensors format instead of pickle-based formats)
Restrict model loading to explicitly trusted and verified sources only

bash

# Upgrade picklescan to patched version
pip install --upgrade picklescan>=0.0.22

# Verify installed version
pip show picklescan | grep Version

# Re-scan previously approved models
picklescan --path /path/to/models/