Comprehensive Technical Analysis of EUVD-2025-6954
1. Vulnerability Assessment and Severity Evaluation
The EUVD entry EUVD-2025-6954 pertains to a deserialization vulnerability in PyTorch, a popular open-source machine learning library. The vulnerability has a CVSS Base Score of 9.8, indicating a critical severity level. The CVSS vector CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H breaks down as follows:
- Attack Vector (AV): Network (N) - The vulnerability can be exploited remotely over the network.
- Attack Complexity (AC): Low (L) - The attack requires minimal skill or resources to exploit.
- Privileges Required (PR): None (N) - No special privileges are needed to exploit the vulnerability.
- User Interaction (UI): None (N) - No user interaction is required for the attack to succeed.
- Scope (S): Unchanged (U) - The vulnerability does not change the security scope.
- Confidentiality (C): High (H) - The vulnerability allows for significant breaches of confidentiality.
- Integrity (I): High (H) - The vulnerability allows for significant breaches of integrity.
- Availability (A): High (H) - The vulnerability allows for significant breaches of availability.
Given the high scores in confidentiality, integrity, and availability, this vulnerability poses a severe risk to systems using PyTorch.
2. Potential Attack Vectors and Exploitation Methods
The deserialization vulnerability in PyTorch can be exploited through several attack vectors:
- Remote Code Execution (RCE): An attacker could craft malicious serialized data that, when deserialized, executes arbitrary code on the target system.
- Data Manipulation: An attacker could manipulate serialized data to alter the state of the application, leading to unauthorized actions or data corruption.
- Denial of Service (DoS): An attacker could send specially crafted serialized data that causes the application to crash or become unresponsive.
Exploitation methods may include:
- Network Interception: Intercepting and modifying serialized data transmitted over the network.
- Malicious Inputs: Providing malicious serialized data through user inputs or API endpoints.
- Supply Chain Attacks: Compromising dependencies or libraries that interact with PyTorch to inject malicious serialized data.
3. Affected Systems and Software Versions
The vulnerability affects PyTorch versions up to and including the latest version at the time of the advisory. Specifically, the ENISA ID Product entry indicates that all versions up to the latest are affected. Organizations using PyTorch for machine learning tasks, especially those involving distributed computing or remote procedure calls (RPC), are at risk.
4. Recommended Mitigation Strategies
To mitigate the risk associated with this vulnerability, the following strategies are recommended:
- Update PyTorch: Ensure that all instances of PyTorch are updated to a version that includes a fix for this vulnerability. Monitor the PyTorch GitHub repository for updates and patches.
- Input Validation: Implement robust input validation and sanitization for all serialized data. Ensure that only trusted sources are allowed to provide serialized data.
- Network Security: Use secure communication channels (e.g., TLS) to protect serialized data transmitted over the network.
- Access Controls: Implement strict access controls to limit who can provide serialized data to the application.
- Monitoring and Logging: Enhance monitoring and logging to detect and respond to suspicious activities related to serialized data processing.
5. Impact on European Cybersecurity Landscape
The vulnerability in PyTorch has significant implications for the European cybersecurity landscape, particularly for organizations involved in machine learning and artificial intelligence. The widespread use of PyTorch in research, industry, and government sectors means that a broad range of systems could be affected. The high severity of the vulnerability underscores the need for vigilant cybersecurity practices and timely patch management.
6. Technical Details for Security Professionals
For security professionals, the following technical details are pertinent:
- Vulnerable Code: The vulnerability is located in the
internal.pyfile of the PyTorch repository, specifically around line 162. This file is part of the distributed RPC module, which handles serialized data. - References:
- NVD Entry: CVE-2024-7804
- Huntr Bounty: Huntr Bounty
- PyTorch GitHub: PyTorch Repository
- Vulnerable Code: internal.py
Security professionals should review these references for detailed information on the vulnerability and any available patches or workarounds.
Conclusion
The deserialization vulnerability in PyTorch, as described in EUVD-2025-6954, is a critical issue that requires immediate attention. Organizations should prioritize updating PyTorch and implementing robust security measures to mitigate the risk. The European cybersecurity community must remain vigilant and proactive in addressing such vulnerabilities to protect against potential attacks.