CVE-2024-12029

Comprehensive Technical Analysis of CVE-2024-12029

1. Vulnerability Assessment and Severity Evaluation

CVE ID: CVE-2024-12029 CVSS Score: 9.8

The vulnerability in question is a remote code execution (RCE) flaw in the invoke-ai/invokeai software, specifically affecting versions 5.3.1 through 5.4.2. The issue arises from unsafe deserialization of model files using torch.load without proper validation. This allows attackers to embed malicious code within model files, which is executed upon loading.

Severity Evaluation:

• CVSS Score: 9.8 (Critical)
• Impact: High
• Exploitability: High

The high CVSS score indicates a critical vulnerability that can be easily exploited with severe consequences. The ability to execute arbitrary code remotely poses significant risks, including data breaches, system compromise, and potential lateral movement within a network.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Malicious Model Files: Attackers can craft model files containing malicious code and upload them via the /api/v2/models/install API.
• Supply Chain Attacks: Compromising the integrity of model files distributed through legitimate channels.

Exploitation Methods:

• Embedding Malicious Code: Attackers can embed Python code within the model files that will be executed when the model is loaded using torch.load.
• Remote Code Execution: Once the malicious model file is loaded, the embedded code can perform various malicious actions, such as data exfiltration, system compromise, or establishing a backdoor.

3. Affected Systems and Software Versions

Affected Software:

• invoke-ai/invokeai versions 5.3.1 through 5.4.2

Affected Systems:

• Any system running the vulnerable versions of invoke-ai/invokeai that processes model files through the /api/v2/models/install API.

4. Recommended Mitigation Strategies

Immediate Mitigation:

• Upgrade to Version 5.4.3: The vulnerability is fixed in version 5.4.3. Organizations should upgrade to this version immediately.
• Disable Model Installation API: Temporarily disable the /api/v2/models/install API until the upgrade is complete.

Long-Term Mitigation:

• Input Validation: Implement robust input validation and sanitization for all model files.
• Secure Deserialization: Use secure deserialization methods and avoid torch.load for untrusted data.
• Regular Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.

5. Impact on Cybersecurity Landscape

Immediate Impact:

• Increased Risk of RCE Attacks: Organizations using the affected software are at high risk of RCE attacks, which can lead to significant data breaches and system compromises.
• Supply Chain Risks: The vulnerability highlights the risks associated with supply chain attacks, where malicious code can be embedded in seemingly legitimate files.

Long-Term Impact:

• Enhanced Focus on Deserialization Security: This incident underscores the importance of secure deserialization practices and the need for robust input validation.
• Increased Awareness: The cybersecurity community will likely see increased awareness and scrutiny of similar vulnerabilities in other software.

6. Technical Details for Security Professionals

Vulnerability Details:

• Root Cause: Unsafe deserialization using torch.load without proper validation.
• Exploitation: Malicious code embedded in model files is executed upon loading.

Detection and Response:

• Monitoring: Implement monitoring for unusual activities related to model file uploads and API calls.
• Logging: Ensure comprehensive logging of all API requests and model file operations.
• Incident Response: Develop and test incident response plans for RCE attacks, including containment, eradication, and recovery procedures.

References:

• GitHub Commit
• Huntr Bounty

By addressing this vulnerability promptly and implementing robust security measures, organizations can mitigate the risks associated with CVE-2024-12029 and enhance their overall cybersecurity posture.