CVE-2024-0959: Stanford GibsonEnv RCE Vulnerability

CVE-2024-0959 Overview

A critical insecure deserialization vulnerability has been identified in StanfordVL GibsonEnv version 0.3.1, affecting the cloudpickle.load function within the file gibson/utils/pposgd_fuse.py. This vulnerability allows remote attackers to potentially execute arbitrary code by exploiting unsafe deserialization of untrusted data. The use of cloudpickle.load without proper validation enables malicious actors to craft specially prepared pickle files that, when loaded, can trigger arbitrary code execution on the target system.

Critical Impact

Remote code execution through insecure deserialization of pickle files could allow complete system compromise, data theft, and lateral movement within affected environments.

Affected Products

• StanfordVL GibsonEnv 0.3.1
• Applications utilizing the gibson/utils/pposgd_fuse.py module
• Systems loading untrusted pickle data through the vulnerable function

Discovery Timeline

• 2024-01-27 - CVE-2024-0959 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2024-0959

Vulnerability Analysis

The vulnerability stems from the use of Python's cloudpickle.load function to deserialize data without proper input validation or integrity checks. Python pickle modules, including cloudpickle, are inherently unsafe when processing untrusted data because they can execute arbitrary code during the deserialization process. When an attacker provides a malicious pickle file, the cloudpickle.load function will execute the embedded payload, leading to remote code execution.

The affected function resides in gibson/utils/pposgd_fuse.py, which is part of the GibsonEnv robotics simulation environment. This component is typically used for loading pretrained models and datasets, making it a prime target for supply chain attacks where malicious pickle files could be distributed as legitimate datasets.

Root Cause

The root cause is CWE-502: Deserialization of Untrusted Data. The application uses cloudpickle.load to deserialize data without implementing proper safeguards such as:

• Signature verification of pickle files
• Allowlist-based class filtering during deserialization
• Sandboxed execution environments for loading untrusted data
• Input validation before deserialization

Python pickle serialization was never designed to be secure against malicious input, and the documentation explicitly warns against loading pickle data from untrusted sources.

Attack Vector

The attack is network-based and can be executed remotely. An attacker can exploit this vulnerability by:

1. Crafting a malicious pickle file containing embedded Python code
2. Distributing the malicious file as a seemingly legitimate dataset or model
3. Tricking a victim into loading the malicious pickle file through the GibsonEnv application
4. Achieving code execution when cloudpickle.load processes the malicious payload

The malicious pickle payload typically uses the __reduce__ method to specify arbitrary functions to execute during deserialization. A proof of concept demonstrating this vulnerability has been disclosed publicly. Technical details and a sample malicious dataset file are available in the GitHub Proof of Concept repository.

Detection Methods for CVE-2024-0959

Indicators of Compromise

• Unexpected network connections originating from GibsonEnv processes
• Suspicious child processes spawned by Python interpreters running GibsonEnv
• Presence of unfamiliar or recently downloaded .pkl files in working directories
• Unusual file system activity following the loading of pickle datasets

Detection Strategies

• Monitor for execution of cloudpickle.load or pickle.load on untrusted files through application logging
• Implement file integrity monitoring on directories where pickle files are stored
• Deploy endpoint detection solutions to identify suspicious process chains initiated by Python processes
• Audit incoming data files for unexpected pickle objects using static analysis tools

Monitoring Recommendations

• Enable verbose logging for all pickle loading operations within GibsonEnv deployments
• Implement network segmentation to limit outbound connectivity from systems running GibsonEnv
• Use behavioral analysis to detect anomalous activity following dataset loading operations
• Review system audit logs for unexpected privilege escalation or persistence mechanisms

How to Mitigate CVE-2024-0959

Immediate Actions Required

• Avoid loading pickle files from untrusted or unverified sources
• Implement cryptographic signature verification for all dataset and model files before loading
• Consider migrating to safer serialization formats such as JSON, YAML, or Protocol Buffers where applicable
• Isolate GibsonEnv deployments in sandboxed or containerized environments with restricted privileges

Patch Information

No official vendor patch has been identified at this time. Users should monitor the StanfordVL GibsonEnv repository for security updates. For detailed vulnerability tracking information, refer to the VulDB entry #252204.

Workarounds

• Replace cloudpickle.load with safer alternatives that implement class allowlisting
• Add wrapper functions that verify file signatures before any pickle deserialization
• Run GibsonEnv in isolated Docker containers with minimal privileges and no network access
• Implement application-level firewalls to restrict pickle loading to pre-approved file paths only

# Example: Restrict pickle file loading to verified sources only
# Add hash verification before loading pickle files
sha256sum --check checksums.sha256 dataset.pkl
# If verification fails, do not proceed with loading
# Consider using safer serialization formats where possible