µLAM: A LLM-Powered Assistant for Real-Time Micro-architectural Attack Detection and Mitigation

The rise of microarchitectural attacks has necessitated robust detection and mitigation strategies to secure computing systems. Traditional tools, such as static and dynamic code analyzers and attack detectors, often fall short due to their reliance on predefined patterns and heuristics that lack the flexibility to adapt to new or evolving attack vectors. In this paper, we introduce for the first time a microarchitecture security assistant, built on OpenAI's GPT-3.5, which we refer to as μLAM. This assistant surpasses conventional tools by not only identifying vulnerable code segments but also providing context-aware mitigations, tailored to specific system specifications and existing security measures. Additionally, μLAM leverages real-time data from dynamic Hardware Performance Counters (HPCs) and system specifications to detect ongoing attacks, offering a level of adaptability and responsiveness that static and dynamic analyzers cannot match.