CVE-2025-54949

Comprehensive Technical Analysis of CVE-2025-54949

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-54949

Description: A heap buffer overflow vulnerability exists in the loading of ExecuTorch models. This flaw can potentially lead to arbitrary code execution or other undesirable effects. The vulnerability affects ExecuTorch prior to commit ede82493dae6d2d43f8c424e7be4721abe5242be.

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 have severe consequences including system compromise, data breaches, 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 heap buffer overflow.
• Supply Chain Attacks: Compromising the distribution channels of ExecuTorch models could lead to widespread exploitation.
• Phishing and Social Engineering: Tricking users into downloading and loading malicious model files from untrusted sources.

Exploitation Methods:

• Heap Buffer Overflow: By exploiting the heap buffer overflow, an attacker can overwrite adjacent memory, leading to code execution or crashing the application.
• Memory Corruption: The attacker could manipulate memory to alter the control flow of the application, leading to unauthorized actions.

3. Affected Systems and Software Versions

Affected Software:

• ExecuTorch versions prior to the commit ede82493dae6d2d43f8c424e7be4721abe5242be.

Systems:

• Any system running the affected 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

Immediate Actions:

• Update Software: Ensure that all instances of ExecuTorch are updated to versions that include the commit ede82493dae6d2d43f8c424e7be4721abe5242be or later.
• Input Validation: Implement strict validation and sanitization of model files before loading them into ExecuTorch.
• Access Controls: Restrict access to model loading functionalities to trusted users and systems.

Long-Term Strategies:

• Regular Patching: Establish a routine for regular updates and patching of all software components.
• Security Training: Educate developers and users on the risks associated with loading untrusted model files.
• Monitoring and Logging: Enhance monitoring and logging to detect and respond to any suspicious activities related to model loading.

5. Impact on Cybersecurity Landscape

Immediate Impact:

• Increased Risk: Organizations using ExecuTorch are at increased risk of exploitation, which could lead to data breaches, service disruptions, and financial losses.
• Supply Chain Vulnerabilities: Highlights the importance of securing the entire software supply chain, including third-party libraries and dependencies.

Long-Term Impact:

• Enhanced Security Measures: Likely to prompt organizations to adopt more stringent security measures for machine learning frameworks.
• Industry Awareness: Raises awareness about the potential security risks in AI/ML frameworks, leading to more robust security practices.

6. Technical Details for Security Professionals

Vulnerability Details:

• Heap Buffer Overflow: The vulnerability occurs due to improper bounds checking during the loading of ExecuTorch models, allowing an attacker to overwrite memory.
• Exploitation: An attacker can craft a model file with a payload that, when loaded, overflows the heap buffer and executes arbitrary code.

Detection and Response:

• Memory Analysis: Use memory analysis tools to detect anomalies in heap memory usage during model loading.
• Intrusion Detection Systems (IDS): Deploy IDS to monitor for unusual network traffic patterns that may indicate an exploitation attempt.
• Incident Response Plan: Develop and maintain an incident response plan tailored to handle heap buffer overflow vulnerabilities in machine learning frameworks.

References:

• GitHub Commit
• Facebook Security Advisory

By addressing this vulnerability promptly and comprehensively, organizations can mitigate the risks associated with CVE-2025-54949 and enhance their overall cybersecurity posture.