Paper-Conference

Abstract There is a growing interest in leveraging large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering.

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 Voice assistants (VAs) have become integral to daily life, yet their always-on microphones make them attractive targets for attacks that threaten user privacy and safety. We present MetaGuardian, the first system to leverage acoustic metamaterials to defend against three major classes of attacks for VAs—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for evaluating LLMs in bug detection and repair, existing studies rely on hand-crafted datasets that quickly become outdated.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.