CVE-2025-54952

Comprehensive Technical Analysis of CVE-2025-54952

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-54952

Description: An integer overflow vulnerability in the loading of ExecuTorch models can cause smaller-than-expected memory regions to be allocated, potentially resulting in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit 8f062d3f661e20bb19b24b767b9a9a46e8359f2b.

CVSS Score: 9.8

Severity Evaluation: The CVSS score of 9.8 indicates a critical vulnerability. This high score is due to the potential for remote code execution, which can lead to complete system compromise. The vulnerability's impact on confidentiality, integrity, and availability is severe, making it a high-priority issue for immediate remediation.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

1. Malicious Model Files: An attacker could craft a specially designed ExecuTorch model file that, when loaded, triggers the integer overflow.
2. Supply Chain Attacks: Compromising the distribution channels of ExecuTorch models to inject malicious files.
3. Phishing and Social Engineering: Tricking users into downloading and loading compromised model files from untrusted sources.

Exploitation Methods:

1. Memory Corruption: By exploiting the integer overflow, an attacker can manipulate memory allocations, leading to buffer overflows and arbitrary code execution.
2. Privilege Escalation: If the vulnerable software runs with elevated privileges, an attacker could gain higher-level access to the system.
3. Data Exfiltration: Once code execution is achieved, an attacker could exfiltrate sensitive data or install additional malware.

3. Affected Systems and Software Versions

Affected Software:

• ExecuTorch prior to commit 8f062d3f661e20bb19b24b767b9a9a46e8359f2b.

Affected Systems:

• Any system running the vulnerable versions of ExecuTorch, including but not limited to:
  • Development and production environments using ExecuTorch for model deployment.
  • Cloud-based machine learning platforms integrating ExecuTorch.
  • Research and academic institutions utilizing ExecuTorch for model training and inference.

4. Recommended Mitigation Strategies

1. Immediate Patching:

   • Update ExecuTorch to the version that includes the commit 8f062d3f661e20bb19b24b767b9a9a46e8359f2b or later.

2. Input Validation:

   • Implement strict validation checks on model files before loading them into ExecuTorch.
   • Use digital signatures or checksums to verify the integrity of model files.

3. Access Control:

   • Restrict access to model loading functionalities to trusted users and processes.
   • Ensure that ExecuTorch runs with the least privilege necessary.

4. Network Segmentation:

   • Isolate systems running ExecuTorch from other critical systems to limit the potential impact of a compromise.

5. Monitoring and Logging:

   • Enable comprehensive logging and monitoring of model loading activities to detect and respond to suspicious behavior.

5. Impact on Cybersecurity Landscape

Immediate Impact:

• Organizations relying on ExecuTorch for machine learning tasks are at risk of severe security breaches, including data theft, unauthorized access, and system compromise.
• The vulnerability highlights the importance of securing machine learning frameworks, which are increasingly integrated into critical business operations.

Long-Term Impact:

• Increased scrutiny on the security of machine learning and AI frameworks.
• Potential regulatory and compliance implications for organizations failing to address such vulnerabilities promptly.
• Enhanced focus on secure coding practices and robust testing methodologies within the machine learning community.

6. Technical Details for Security Professionals

Vulnerability Details:

• The integer overflow occurs during the memory allocation process for loading ExecuTorch models.
• The flaw allows an attacker to allocate a smaller memory region than expected, leading to potential buffer overflows and code execution.

Detection and Response:

• Detection: Implement intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor for unusual memory allocation patterns and suspicious model loading activities.
• Response: Develop incident response plans specific to machine learning framework vulnerabilities, including steps for containment, eradication, and recovery.

Code Review and Testing:

• Conduct thorough code reviews focusing on memory allocation and handling of model files.
• Implement fuzz testing and static analysis tools to identify similar vulnerabilities in other parts of the codebase.

Community and Collaboration:

• Engage with the cybersecurity community and contribute to open-source projects to share knowledge and best practices for securing machine learning frameworks.
• Participate in vulnerability disclosure programs to responsibly report and address security issues.

By addressing this vulnerability promptly and comprehensively, organizations can mitigate the risks associated with CVE-2025-54952 and enhance the overall security posture of their machine learning environments.