1. Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications
- Author
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Musser, Micah, Lohn, Andrew, Dempsey, James X., Spring, Jonathan, Kumar, Ram Shankar Siva, Leong, Brenda, Liaghati, Christina, Martinez, Cindy, Grant, Crystal D., Rohrer, Daniel, Frase, Heather, Elliott, Jonathan, Bansemer, John, Rodriguez, Mikel, Regan, Mitt, Chowdhury, Rumman, and Hermanek, Stefan
- Subjects
FOS: Computer and information sciences ,Computer Science - Computers and Society ,Computer Science - Cryptography and Security ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computers and Society (cs.CY) ,Cryptography and Security (cs.CR) - 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.
- Published
- 2023
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