Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications
Abstract
In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship between vulnerabilities in artificial intelligence systems and more traditional types of software vulnerabilities. Topics discussed included the extent to which AI vulnerabilities can be handled under standard cybersecurity processes, the barriers currently preventing the accurate sharing of information about AI vulnerabilities, legal issues associated with adversarial attacks on AI systems, and potential areas where government support could improve AI vulnerability management and mitigation. This report is meant to accomplish two things. First, it provides a high-level discussion of AI vulnerabilities, including the ways in which they are disanalogous to other types of vulnerabilities, and the current state of affairs regarding information sharing and legal oversight of AI vulnerabilities. Second, it attempts to articulate broad recommendations as endorsed by the majority of participants at the workshop.
Cite
@article{arxiv.2305.14553,
title = {Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications},
author = {Micah Musser and Andrew Lohn and James X. Dempsey and Jonathan Spring and Ram Shankar Siva Kumar and Brenda Leong and Christina Liaghati and Cindy Martinez and Crystal D. Grant and Daniel Rohrer and Heather Frase and Jonathan Elliott and John Bansemer and Mikel Rodriguez and Mitt Regan and Rumman Chowdhury and Stefan Hermanek},
journal= {arXiv preprint arXiv:2305.14553},
year = {2023}
}