CVE-2025-50461 is a deserialization vulnerability identified in Volcengine's verl 3.0.0, specifically within the scripts/model_merger.py script when operating with the "fsdp" backend. The vulnerability arises because the script uses the PyTorch function torch.load() with the parameter weights_only set to False on user-supplied .pt files. This function call deserializes the entire model file, including potentially malicious code embedded within the serialized data. An attacker can exploit this by tricking a victim into downloading and placing a crafted malicious model file in a local directory with a filename pattern expected by the script. When the script loads this file, the malicious payload executes arbitrary code with the same privileges as the user running the script. This form of attack leverages unsafe deserialization, a common vector for remote code execution (RCE) vulnerabilities, especially in machine learning workflows where model files are often loaded dynamically. Since the vulnerability depends on user interaction to place the malicious file and the use of a specific backend, exploitation requires some level of social engineering or insider access. However, once exploited, it can lead to full compromise of the affected system, including data theft, system manipulation, or lateral movement within a network. No patches or fixes have been published yet, and no known exploits are currently observed in the wild. The lack of a CVSS score indicates the vulnerability is newly disclosed and requires immediate attention from users of Volcengine verl 3.0.0 who utilize the fsdp backend in their model merging workflows.

For European organizations, this vulnerability poses a significant risk, particularly for entities involved in AI research, machine learning development, and data science operations that rely on Volcengine's verl 3.0.0 or similar workflows. Exploitation could lead to arbitrary code execution, resulting in unauthorized access to sensitive data, disruption of AI model training or deployment pipelines, and potential compromise of broader IT infrastructure. Given the increasing adoption of AI and machine learning in sectors such as finance, healthcare, manufacturing, and government within Europe, the impact could extend to critical services and intellectual property theft. Additionally, organizations subject to strict data protection regulations like GDPR may face compliance violations and reputational damage if this vulnerability leads to data breaches. The requirement for user interaction to place the malicious file somewhat limits the attack surface but does not eliminate the risk, especially in environments where users have write access to directories used by the script. The absence of known exploits in the wild provides a window for proactive mitigation but should not lead to complacency.

To mitigate this vulnerability, European organizations should take several specific actions beyond generic patching advice: 1) Immediately audit and restrict write permissions on directories where model files are stored or loaded, ensuring only trusted users can place files there. 2) Implement strict validation and integrity checks on all model files before loading, such as cryptographic signatures or hashes, to prevent unauthorized or tampered files from being processed. 3) Modify or patch the model_merger.py script to use torch.load() with weights_only=True if feasible, or employ safer deserialization methods that avoid executing arbitrary code embedded in model files. 4) Educate users and developers about the risks of loading untrusted model files and enforce policies that prohibit downloading models from unverified sources. 5) Monitor systems for unusual file placement activities and anomalous script executions that could indicate exploitation attempts. 6) Engage with Volcengine or the community to obtain patches or updates addressing this vulnerability as soon as they become available. 7) Consider isolating the environment where model merging occurs, using containerization or sandboxing to limit the impact of potential code execution.