Journal of Systems Research

Articles

• [SOK] Large Language Models in Security Code Review and Testing

  • Beljulji, Edin;
  • Gür, Gürkan

  In this paper, we systematically present and discuss practical applications of Large Language Models (LLMs) in software security, concretely in code vulnerability detection, fuzz testing, and exploit generation. Measurements of various research outcomes are analyzed to answer questions about the performance of LLMs in those fields, including a comparison with tools that follow traditional approaches. In addition, the drawbacks and a future outlook, along with a delineation of technical challenges, are provided. Challenges include the cost- and time-intensive training of LLMs, the limited context-length understanding of program code, the high false positive rate due to hallucinations, and keeping the data up-to-date so that definitions of newly detected vulnerabilities are covered.

• [SoK] Systematizing Inference Placement For Deep Learning Across Edge And Cloud Platforms: A Multi-Objective Optimization Perspective

  • Zhang, Zongshun;
  • Matta, Ibrahim

  Edge intelligent applications like VR/AR and language model based chatbots have become widespread with the rapid expansion of IoT and mobile devices. However, constrained edge devices often cannot serve the increasingly large and complex deep learning (DL) models. To mitigate these challenges, researchers have proposed optimizing and offloading partitions of DL models among user devices, edge servers, and the cloud. In this setting, users can take advantage of different services to support their intelligent applications. For example, edge resources offer low response latency. In contrast, cloud platforms provide low monetary cost computation resources for computation-intensive workloads. However, communication between DL model partitions can introduce transmission bottlenecks and pose risks of data leakage. Recent research aims to balance accuracy, computation delay, transmission delay, and privacy concerns. They address these issues with model compression, model distillation, transmission compression, and model architecture adaptations, including internal classifiers. This survey contextualizes the state-of-the-art model offloading methods and model adaptation techniques by studying their implication to a multi-objective optimization comprising inference latency, data privacy, and resource monetary cost.