CVE-2025-54950

Comprehensive Technical Analysis of CVE-2025-54950

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-54950 Description: An out-of-bounds access vulnerability in the loading of ExecuTorch models can cause the runtime to crash and potentially result in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit b6b7a16df5e7852d976d8c34c8a7e9a1b6f7d005. 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 significant impacts such as data breaches, system compromise, and loss of service availability.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Malicious Model Files: An attacker could craft a specially designed ExecuTorch model file that, when loaded, triggers the out-of-bounds access vulnerability.
• Supply Chain Attacks: Compromising the integrity of model files distributed through legitimate channels.
• Phishing and Social Engineering: Tricking users into downloading and loading malicious model files.

Exploitation Methods:

• Buffer Overflow: By exploiting the out-of-bounds access, an attacker could overwrite adjacent memory, leading to arbitrary code execution.
• Denial of Service (DoS): Causing the runtime to crash, resulting in service disruption.
• Privilege Escalation: If the vulnerable application runs with elevated privileges, an attacker could gain higher-level access to the system.

3. Affected Systems and Software Versions

Affected Software:

• ExecuTorch versions prior to the commit b6b7a16df5e7852d976d8c34c8a7e9a1b6f7d005.

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.
  • Edge devices and IoT systems utilizing ExecuTorch for on-device inference.

4. Recommended Mitigation Strategies

Immediate Actions:

• Patching: Upgrade to the latest version of ExecuTorch that includes the fix for this vulnerability (commit b6b7a16df5e7852d976d8c34c8a7e9a1b6f7d005 or later).
• Input Validation: Implement strict validation and sanitization of model files before loading them into the runtime.
• Access Controls: Restrict access to model loading functionalities to trusted users and processes.

Long-Term Strategies:

• Regular Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.
• Security Training: Educate developers and users about the risks associated with loading untrusted model files.
• Incident Response Plan: Develop and maintain an incident response plan to quickly address any potential exploitation of this vulnerability.

5. Impact on Cybersecurity Landscape

Immediate Impact:

• Organizations relying on ExecuTorch for critical applications may face significant risks, including data breaches and service disruptions.
• Increased scrutiny on the security of machine learning frameworks and model deployment pipelines.

Long-Term Impact:

• Greater emphasis on secure coding practices and input validation in machine learning frameworks.
• Potential regulatory scrutiny and compliance requirements for organizations using machine learning technologies.
• Enhanced awareness and investment in cybersecurity measures within the AI/ML community.

6. Technical Details for Security Professionals

Vulnerability Details:

• Type: Out-of-bounds access
• Location: Model loading functionality in ExecuTorch
• Trigger: Loading a specially crafted model file

Exploitation Steps:

1. Crafting Malicious Model: An attacker creates a model file designed to trigger the out-of-bounds access.
2. Distribution: The malicious model is distributed through phishing, supply chain attacks, or other means.
3. Loading the Model: The victim loads the malicious model file into the ExecuTorch runtime.
4. Exploitation: The out-of-bounds access is triggered, leading to potential code execution or runtime crash.

Detection and Monitoring:

• Log Analysis: Monitor logs for unusual crashes or errors related to model loading.
• Anomaly Detection: Implement anomaly detection mechanisms to identify unusual patterns in model loading activities.
• Intrusion Detection Systems (IDS): Deploy IDS to detect and alert on suspicious activities related to model file handling.

Conclusion: CVE-2025-54950 represents a critical vulnerability in ExecuTorch that requires immediate attention. Organizations should prioritize patching and implementing robust security measures to mitigate the risks associated with this vulnerability. The broader cybersecurity community should take this as an opportunity to enhance the security of machine learning frameworks and deployment pipelines.