AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective

ArXi:2603.24857v1 Announce Type: cross As machine learning (ML) systems expand in both scale and functionality, the security landscape has become increasingly complex, with a proliferation of attacks and defenses. However, existing studies largely treat these threats in isolation, lacking a coherent framework to expose their shared principles and interdependencies. This fragmented view hinders systematic understanding and limits the design of comprehensive defenses.