With the emergence of new AI technologies, research on the potential for AI to function as teammates alongside humans has expanded. The recent introduction of highly capable large language models (LLMs) is particularly noteworthy, showing strong potential in human–AI teaming where communication is crucial. However, this novel technology has yet to be validated in human–AI teaming or as a teammate, hindering its application in research and practice. This article presents an empirical online experiment (N = 778) where participants engaged in a real-time and interdependent interaction with a commercially available LLM, with the presentation of the LLM manipulated to be either a tool or a teammate. Results show that when compared to presenting an LLM as a teammate rather than a tool significantly increases trust and significantly impacts the sentiment humans have when talking with their AI, with LLM teammates seeing more positive sentiment. Perceptions of trust, acceptance, and performance were generally high for LLMs presented as teammates. Despite these impacts, participants’ prior experiences with AI technology were still shown to predict the perceptions they formed with their AI teammate. Based on these findings, this article presents an important empirical result, which is that presenting highly capable AI, such as LLMs, as teammates can improve perception and interaction compared to presenting an AI as a tool. In turn, a discussion is had on how future research can continue to identify when and how to introduce LLMs and other AI technologies as teammates.

Recent research utilizing Secure Virtual Architecture (SVA) has demonstrated that compiler-based virtual machines can protect applications from side-channel attacks launched by compromised operating system kernels. However, SVA provides no instructions for using hardware virtualization features such as Intel’s Virtual Machine Extensions (VMX) and AMD’s Secure Virtual Machine (SVM). Consequently, operating systems running on top of SVA cannot run guest operating systems using features such as Linux’s Kernel Virtual Machine (KVM) and FreeBSD’s bhyve.

The challenges faced in securing embedded computing systems against multifaceted memory safety vulnerabilities have prompted great interest in the development of memory safety countermeasures. These countermeasures either provide protection only against their corresponding type of vulnerabilities, or incur substantial architectural modifications and overheads in order to provide complete safety, which makes them infeasible for embedded systems. In this paper, we propose M-MAP: a comprehensive system based on multi-factor memory authentication for complete memory safety. We examine certain crucial implications of composing memory integrity verification and bounds checking schemes in a comprehensive system. Based on these implications, we implement M-MAP with hardware based memory integrity verification and software based bounds checking to achieve a balance between hardware modifications and performance. We demonstrate that M-MAP implemented on top of a lightweight out-of-order processor delivers complete memory safety with only 32% performance overhead on average, while incurring minimal hardware modifications, and area overhead.