CVE-2025-30405

Comprehensive Technical Analysis of CVE-2025-30405

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-30405

Description: An integer overflow vulnerability in the loading of ExecuTorch models can cause objects to be placed outside their allocated memory area, potentially resulting in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit 0830af8207240df8d7f35b984cdf8bc35d74fa73.

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 integer overflow can be exploited to write arbitrary data to memory, leading to a variety of severe security issues.

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 integer overflow.
• Supply Chain Attacks: Compromising the integrity of model files distributed through legitimate channels.
• Network-Based Attacks: If the model loading process can be initiated over a network, an attacker could exploit this vulnerability remotely.

Exploitation Methods:

• Buffer Overflow: By causing an integer overflow, an attacker can write data beyond the allocated memory, potentially overwriting critical data structures or executing arbitrary code.
• Heap Spraying: An attacker could use this vulnerability to spray the heap with malicious code, increasing the likelihood of successful exploitation.
• Return-Oriented Programming (ROP): An attacker could chain together small pieces of existing code (gadgets) to perform malicious actions.

3. Affected Systems and Software Versions

Affected Software:

• ExecuTorch versions prior to the commit 0830af8207240df8d7f35b984cdf8bc35d74fa73.

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

4. Recommended Mitigation Strategies

Immediate Actions:

• Update Software: Ensure that all instances of ExecuTorch are updated to versions that include the commit 0830af8207240df8d7f35b984cdf8bc35d74fa73 or later.
• Input Validation: Implement strict validation checks on model files before loading them into memory.
• Memory Protection: Enable and configure memory protection mechanisms such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP).

Long-Term Strategies:

• Code Review: Conduct thorough code reviews to identify and mitigate similar vulnerabilities.
• Security Training: Provide training for developers on secure coding practices to prevent integer overflow and other memory-related issues.
• Regular Audits: Perform regular security audits and vulnerability assessments on all software components.

5. Impact on Cybersecurity Landscape

Immediate Impact:

• Increased Risk: Organizations using ExecuTorch are at increased risk of remote code execution attacks, which can lead to data breaches, system compromises, and other severe security incidents.
• Supply Chain Risks: The vulnerability highlights the risks associated with third-party software and the need for robust supply chain security measures.

Long-Term Impact:

• Enhanced Security Practices: This vulnerability serves as a reminder of the importance of secure coding practices and the need for continuous monitoring and updating of software dependencies.
• Industry Awareness: Increased awareness within the cybersecurity community about the risks associated with machine learning frameworks and the need for proactive security measures.

6. Technical Details for Security Professionals

Vulnerability Details:

• Integer Overflow: The vulnerability occurs due to improper handling of integer values during the model loading process, leading to memory corruption.
• Memory Corruption: The overflow allows an attacker to write data outside the intended memory boundaries, potentially overwriting critical data structures or executing arbitrary code.

Detection and Response:

• Intrusion Detection Systems (IDS): Configure IDS to detect anomalous behavior associated with model loading processes.
• Log Analysis: Monitor logs for unusual patterns or errors related to memory allocation and model loading.
• Incident Response: Develop and implement an incident response plan that includes steps for identifying, containing, and remediating exploitation attempts.

References:

• GitHub Commit
• Facebook Security Advisory

By addressing this vulnerability promptly and implementing robust security measures, organizations can mitigate the risks associated with CVE-2025-30405 and enhance their overall cybersecurity posture.