CVE-2025-30404

Comprehensive Technical Analysis of CVE-2025-30404

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-30404 CVSS Score: 9.8

The vulnerability in question is an integer overflow issue in the loading of ExecuTorch models, which can lead to overlapping allocations. This type of vulnerability is particularly severe because it can result in arbitrary code execution or other undesirable effects, such as data corruption or system crashes. The CVSS score of 9.8 indicates a critical severity level, underscoring the potential for significant impact if exploited.

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 distribution channels of ExecuTorch models could allow attackers to inject malicious models into legitimate software updates or downloads.

Exploitation Methods:

• Buffer Overflow: By exploiting the integer overflow, an attacker could manipulate memory allocations to overwrite adjacent memory, potentially leading to code execution.
• Heap Spraying: An attacker could use the vulnerability to spray the heap with malicious code, increasing the likelihood of successful exploitation.

3. Affected Systems and Software Versions

Affected Software:

• ExecuTorch prior to commit d158236b1dc84539c1b16843bc74054c9dcba006.

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:

• Update Software: Ensure that all instances of ExecuTorch are updated to the version containing the commit d158236b1dc84539c1b16843bc74054c9dcba006 or later.
• Input Validation: Implement strict validation and sanitization of ExecuTorch model files before loading them.
• Monitoring: Enhance monitoring for unusual memory usage patterns or unexpected behavior in systems running ExecuTorch.

Long-Term Strategies:

• Security Audits: Conduct regular security audits of machine learning frameworks and models.
• Patch Management: Establish a robust patch management process to ensure timely updates and patches are applied.
• Training: Provide training for developers and administrators on secure coding practices and vulnerability management.

5. Impact on Cybersecurity Landscape

The discovery of CVE-2025-30404 highlights the growing importance of securing machine learning frameworks and models. As machine learning becomes more integrated into critical systems, vulnerabilities like this one can have far-reaching consequences, including:

• Data Breaches: Unauthorized access to sensitive data processed by machine learning models.
• System Compromise: Potential for attackers to gain control over systems running vulnerable software.
• Reputation Damage: Organizations relying on ExecuTorch may face reputational risks if their systems are compromised.

6. Technical Details for Security Professionals

Vulnerability Details:

• Root Cause: The integer overflow occurs during the loading of ExecuTorch models, specifically in the memory allocation process.
• Trigger: The vulnerability is triggered when a model file with specially crafted parameters is loaded, causing an overflow in the integer calculations used for memory allocation.

Detection and Response:

• Intrusion Detection Systems (IDS): Configure IDS to detect anomalous memory allocation patterns and unusual network traffic related to ExecuTorch model loading.
• Log Analysis: Analyze logs for any indicators of compromise, such as repeated failed attempts to load models or unexpected system crashes.
• Incident Response: Develop an incident response plan specific to machine learning framework vulnerabilities, including steps for containment, eradication, and recovery.

Conclusion: CVE-2025-30404 represents a critical vulnerability that requires immediate attention from cybersecurity professionals. By understanding the technical details and implementing robust mitigation strategies, organizations can protect their systems from potential exploitation and maintain the integrity of their machine learning operations.