March 12, 2024 at 04:03PM
The rush to develop AI/ML models overlooks their security, risking backdoor and hijacking attacks. Companies lack visibility into their 1,600+ models in production, leaving them vulnerable. Pretrained models from repositories raise security concerns, with potential for attackers to compromise systems. Securing ML operations and assessing model security are crucial in this growing AI ecosystem.
Based on the meeting notes, here are the key takeaways:
1. Security of AI/ML models is often an afterthought, posing significant risks to firms as attackers are targeting these models as a potential vector for compromising companies.
2. Companies significantly lack visibility into their ML assets, with over 1,600 models in production and a lack of control over their AI attack surface due to data scientists and developers downloading models from various sources.
3. Popular frameworks for creating models have file formats capable of executing code, making them vulnerable to attacks. Furthermore, the rush to adopt generative AI has led to the downloading of pretrained models from sites that may not have sufficient security measures.
4. Integration of security throughout the ML pipeline (MLSecOps) is essential. This includes ensuring the security of training data, understanding the ML operations life cycle, and implementing security tools to scan models for malicious code and assess underlying security.
5. Gaining more visibility into the AI/ML pipeline is crucial for preventing model-based attacks and ensuring the safety and security of deployed models.
These takeaways demonstrate the urgency for companies to prioritize AI/ML model security and implement comprehensive security measures throughout the entire ML pipeline.