Paper-Conference

Abstract Capability Hardware Enhanced RISC Instructions (CHERI) offer a hardware-based approach to enhance memory safety by enforcing strong spatial and temporal memory protections. This paper presents the largest performance analysis of the CHERI architecture on the ARM Morello platform seen to date.

Abstract We present MetaGuardian, the first system to leverage acoustic metamaterials to protect voice assistants (VAs) against all three major classes of attacks - inaudible, adversarial, and laser-based - within a single, portable design.

Abstract Large language models (LLMs) hold great promise for automating software vulnerability detection and repair, but ensuring their correctness remains a challenge. While recent work has developed benchmarks for evaluating LLMs in bug detection and repair, existing studies rely on hand-crafted datasets that quickly become outdated.

Abstract Language Models (LMs) are increasingly used for type inference, aiding in error detection and software development. Some real-life deployments of LMs require the model to run on local machines to safeguard software’s intellectual property.

Abstract Real-life deployment of federated Learning (FL) often faces non-IID data, which leads to poor accuracy and slow convergence. Personalized FL (pFL) tackles these issues by tailoring local models to individual data sources and using weighted aggregation methods for client-specific learning.