CVE-2025-49655

Comprehensive Technical Analysis of CVE-2025-49655

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2025-49655 CVSS Score: 9.8

The vulnerability in question pertains to the deserialization of untrusted data in the Keras framework, specifically affecting versions 3.11.0 up to but not including 3.11.3. This vulnerability allows for the execution of arbitrary code on an end user’s system when a maliciously crafted Keras file containing a TorchModuleWrapper class is loaded, even with safe mode enabled. The high CVSS score of 9.8 indicates a critical severity level, reflecting the potential for significant impact if exploited.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Local File Upload: An attacker with local access could upload a malicious Keras file to the system.
• Remote File Upload: An attacker could exploit this vulnerability through remote file upload mechanisms, such as web applications that accept Keras model files from users.

Exploitation Methods:

• Crafting Malicious Files: An attacker can create a Keras file with a TorchModuleWrapper class that contains malicious code.
• Loading the File: When the end user or system loads this file, the malicious code is executed, leading to arbitrary code execution.

3. Affected Systems and Software Versions

Affected Software:

• Keras framework versions 3.11.0 to 3.11.2

Affected Systems:

• Any system running the affected versions of the Keras framework, including but not limited to:
  • Machine learning development environments
  • Production systems deploying Keras models
  • Cloud-based machine learning services using Keras

4. Recommended Mitigation Strategies

Immediate Actions:

• Upgrade Keras: Upgrade to Keras version 3.11.3 or later, which includes the patch for this vulnerability.
• Disable Untrusted File Loading: Temporarily disable the loading of Keras files from untrusted sources until the system is patched.

Long-Term Mitigations:

• Input Validation: Implement robust input validation mechanisms to ensure that only trusted and verified Keras files are loaded.
• Sandboxing: Use sandboxing techniques to isolate the environment where Keras files are loaded and executed.
• Regular Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.

5. Impact on Cybersecurity Landscape

The discovery of this vulnerability highlights the ongoing risks associated with deserialization of untrusted data, a common issue in many software frameworks. It underscores the importance of secure coding practices and the need for continuous monitoring and updating of software dependencies. The high CVSS score indicates that this vulnerability could be exploited to cause significant damage, including data breaches, system compromises, and potential loss of sensitive information.

6. Technical Details for Security Professionals

Vulnerability Details:

• Deserialization Issue: The vulnerability arises from the deserialization process in Keras, where untrusted data is processed without proper validation.
• TorchModuleWrapper Class: The TorchModuleWrapper class is specifically targeted in this vulnerability, allowing for the injection of malicious code.

Detection and Response:

• Logging and Monitoring: Implement comprehensive logging and monitoring to detect any unusual activities related to the loading of Keras files.
• Incident Response Plan: Develop and maintain an incident response plan that includes steps for identifying, containing, and remediating any exploitation of this vulnerability.

References:

• Keras GitHub Pull Request
• HiddenLayer Security Advisor

Conclusion

CVE-2025-49655 represents a critical vulnerability in the Keras framework that can lead to arbitrary code execution. Immediate mitigation involves upgrading to the patched version and implementing strict input validation. Long-term strategies should focus on secure coding practices and continuous security audits to prevent similar issues in the future. The cybersecurity community must remain vigilant against deserialization vulnerabilities and ensure that all software dependencies are regularly updated and monitored.