EUVD-2025-23367

Comprehensive Technical Analysis of EUVD-2025-23367

1. Vulnerability Assessment and Severity Evaluation

The vulnerability in the modelscope/ms-swift library up to version 2.6.1 is classified as a deserialization flaw, specifically within the load_model_meta() function of the ModelFileSystemCache() class. This flaw allows for arbitrary code execution through the use of pickle.load() on untrusted data. The CVSS base score of 9.8 indicates a critical severity level, reflecting the potential for significant impact on confidentiality, integrity, and availability.

CVSS Vector Breakdown:

• AV:N (Network Vector): The vulnerability can be exploited remotely over the network.
• AC:L (Low Complexity): The attack requires minimal skill or resources to exploit.
• PR:N (No Privileges Required): No special privileges are needed to exploit the vulnerability.
• UI:N (No User Interaction): No user interaction is required for the attack to succeed.
• S:U (Unchanged): The scope of the vulnerability does not change.
• C:H (High Confidentiality Impact): The vulnerability can lead to a significant breach of confidentiality.
• I:H (High Integrity Impact): The vulnerability can lead to a significant breach of integrity.
• A:H (High Availability Impact): The vulnerability can lead to a significant disruption in availability.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Malicious Model Files: Attackers can craft a malicious .mdl file that, when loaded, executes arbitrary code.
• Supply Chain Attacks: Compromising the distribution channels of model files to inject malicious payloads.
• Phishing: Tricking users into downloading and using a compromised model file.

Exploitation Methods:

• Deserialization Attack: By embedding malicious code within a serialized .mdl file, attackers can exploit the pickle.load() function to execute arbitrary commands.
• Hidden Payload: The payload file is hidden, making it difficult for victims to detect tampering.
• Stealthy Execution: The normal training process remains unaffected after the malicious code execution, making detection challenging.

3. Affected Systems and Software Versions

Affected Software:

• modelscope/ms-swift library versions up to and including 2.6.1.

Affected Systems:

• Any system that uses the modelscope/ms-swift library for model training or deployment.
• Systems that process untrusted model files, including cloud-based training environments and on-premises servers.

4. Recommended Mitigation Strategies

Immediate Mitigation:

• Upgrade: Upgrade to a patched version of the modelscope/ms-swift library if available.
• Input Validation: Implement strict input validation and sanitization for model files.
• Disable Pickle: Avoid using pickle.load() for deserialization; consider safer alternatives like JSON.

Long-Term Mitigation:

• Code Review: Conduct thorough code reviews to identify and mitigate similar vulnerabilities.
• Security Training: Educate developers on secure coding practices, especially regarding deserialization.
• Monitoring: Implement monitoring and logging to detect unusual activities during model training.

5. Impact on European Cybersecurity Landscape

The vulnerability poses a significant risk to organizations and individuals within the European Union, particularly those involved in machine learning and AI model training. The potential for remote code execution can lead to data breaches, unauthorized access, and disruption of services. Given the critical nature of the vulnerability, it underscores the need for robust cybersecurity measures and continuous monitoring within the EU's digital infrastructure.

6. Technical Details for Security Professionals

Vulnerable Code Snippet:

def load_model_meta(self, file_path):
    with open(file_path, 'rb') as f:
        model_meta = pickle.load(f)
    return model_meta

Exploitation Details:

• Payload Crafting: Attackers can craft a .mdl file containing a serialized payload that includes malicious code.
• Execution Flow: When the load_model_meta() function is called, pickle.load() deserializes the payload, executing the embedded malicious code.

Detection and Response:

• File Integrity Checks: Implement file integrity checks to detect tampering with model files.
• Anomaly Detection: Use anomaly detection systems to identify unusual activities during the model training process.
• Incident Response: Develop an incident response plan to quickly address and mitigate any detected exploitation attempts.

References:

• GitHub Repository
• CVE-2025-50472

By addressing this vulnerability promptly and implementing robust security measures, organizations can significantly reduce the risk of exploitation and protect their digital assets.