AndrewKao
Andrew Kao is a PhD student in economics at Harvard University. His research focuses on the political economy of new technologies, such as AI and the internet. His website is https://andrew-kao.github.io/
Session
As AI continues to reshape global power dynamics, securing AI model weights has become a critical national security challenge. Frontier AI models are expensive to build but cheap to use if they are stolen, making them prime targets for cyber theft. To that end, this talk investigates the security risks of AI model infrastructure, particularly related to AI model weights (the core learned parameters of AI systems). I introduce a tailored scoring framework to assess the likelihood of model theft via three categories: Cyber Exploitation, Insider Threats, and Supply Chain Attacks. Our work builds on MITRE’s ATT&CK and ATLAS frameworks and the 38 attack vectors and five security levels (SL1-SL5) introduced in RAND’s Securing AI Model Weights report. Each category contains several individual attack types, and each attack type is evaluated based on technical feasibility, the effectiveness of existing mitigation strategies, and regulatory gaps. Our results are supplemented with insights from expert interviews spanning cybersecurity, AI, military, intelligence, policy, and legal fields, as well as with existing industry scoring systems like BitSight and RiskRecon. Our research highlights security best practices worth emulating, the most pressing vulnerabilities, and key policy gaps.