Securing the Supply Chain for Open-Source GenAI Models: Best Practices and Real-World Examples

This article, prepared in conjunction with AFCEA’s Technology Committee, is the first in a series of articles addressing supply chain considerations.

In the era of rapid technological advancement, generative AI (GenAI) models have become powerful tools driving innovation across various sectors. However, the very openness and accessibility that make these models so valuable also expose them to security threats that can undermine their integrity, confidentiality and overall reliability. To combat these risks, it's crucial to implement robust security measures throughout the entire supply chain of open-source GenAI models. This article provides a comprehensive guide to securing your GenAI supply chain, illustrated with real-world examples.

Securing the GenAI Supply Chain: Core Principles

• Employ secure version control systems like GitHub with two-factor authentication and restrict code access to authorized developers. Regularly audit and monitor changes to identify potential breaches. As an example, in 2022, a compromised spaCy library highlighted the importance of secure version control. An attacker gained access to a developer account and injected malicious code that could steal sensitive data from users' machines. This incident underscores the need for stringent access controls and continuous monitoring.
• Incorporate thorough code reviews early using automated tools and manual reviews by experienced developers to catch vulnerabilities. As an example, a recent study by OpenAI revealed vulnerabilities in a widely used text-to-image generation model. The vulnerabilities allowed attackers to manipulate the model's output to generate harmful content. This emphasizes the need for robust code reviews, not just by internal teams but also by involving the open-source community.
• Regularly scan dependencies for vulnerabilities using tools like Snyk or Retire.js to identify and address outdated or insecure components. As an example, the 2021 Apache Log4j vulnerability underlines the importance of dependency scanning. This critical vulnerability could have let attackers remotely take control of vulnerable systems. Dependency scanning helps ensure you're not unknowingly introducing security risks through third-party libraries.
• Sign model releases and updates with digital signatures, similar to how TensorFlow verifies releases, to ensure users download genuine code and not a tampered version that could contain malware.
• Use secure build environments offered by many cloud platforms to isolate your code during the build process. As an example, Azure Pipelines offers secure environments to protect your GenAI model during building.
• Distribute models through trusted repositories with enforced security measures or directly from your organization's website using HTTPS for encrypted communication and protected downloads.
• Continuously monitor repositories and channels for signs of compromise and foster a security-aware community by encouraging open communication and responsible vulnerability disclosure. As an example, the GPT-3 team's approach of continuous monitoring and soliciting community feedback is a good practice to follow. By working together, the community can identify and address security concerns more effectively.

Protecting Against Model Poisoning

Model poisoning, where training data is manipulated to introduce vulnerabilities or biases, is a significant threat. Here are some strategies to mitigate it.

• Implement rigorous data validation checks to identify and remove malicious or corrupt data before it affects your model. As an example, the 2016 Tay chatbot incident highlights the importance of data quality assurance.
• Incorporate adversarial training techniques that expose your model to manipulated data during training to make it more robust against real-world poisoning attacks.
• Verify the legitimacy and trustworthiness of data sources used to train your model.
• Apply data augmentation techniques to increase the diversity of the training dataset and reduce the impact of any specific poisoned samples.
• Use anomaly detection mechanisms during training and inference to identify unusual patterns that may indicate model poisoning.
• Conduct thorough model validation and testing to ensure the model performs as expected on clean and unbiased datasets and is robust against various attack types.
• Use models that are interpretable and explainable to identify and understand any unexpected behaviors or biases.
• Secure the model training environment to prevent unauthorized access and potential tampering.
• Implement continuous monitoring of the training pipeline, and conduct regular audits of training data, model parameters and validation processes.
• Collaborate with trusted and reputable data sources to ensure the quality and reliability of training data.
• Implement secure deployment practices with proper authentication and authorization mechanisms to prevent tampering with the deployed model.
• Educate users on secure usage practices and how to verify the integrity of downloaded models and associated files.

By following these best practices and staying vigilant, organizations can significantly enhance the security of GenAI models within their supply chain. Regular updates and adaptation to emerging threats are essential for a robust security strategy. This ensures the continued reliability, fairness and ethical development of these transformative AI technologies.

Sandeep Shilawat is a member of AFCEA International’s Technology Committee. He is an experienced technology leader in federal and fintech sectors, adept at crafting innovative systems and products. He has led pioneering IT modernization projects in classified domains with top secret U.S. Department of Defense clearance, managing multimillion-dollar initiatives and established patented practices in cloud and edge computing, and advising startups in AI, cloud and cybersecurity. He is a recognized industry speaker and author on IT modernization.

Connect with Shilawat on LinkedIn