VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching

Large Language Models (LLMs) have shown promise in tasks like code translation, prompting interest in their potential for automating software vulnerability detection (SVD) and patching (SVP). To further research in this area, establishing a benchmark is essential for evaluating the strengths and limitations of LLMs in these tasks. Despite their capabilities, questions remain regarding whether LLMs can accurately analyze complex vulnerabilities and generate appropriate patches. This paper introduces VulnLLMEval, a framework designed to assess the performance of LLMs in identifying and patching vulnerabilities in C code. Our study includes 307 real-world vulnerabilities extracted from the Linux kernel, creating a well-curated dataset that includes both vulnerable and patched code. This dataset, based on real-world code, provides a diverse and representative testbed for evaluating LLM performance in SVD and SVP tasks, offering a robust foundation for rigorous assessment. Our results reveal that LLMs often struggle with distinguishing between vulnerable and patched code. Furthermore, in SVP tasks, these models tend to oversimplify the code, producing solutions that may not be directly usable without further refinement.

A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities

In this study, we conduct a comprehensive review of smart grid security, exploring system architectures, attack methodologies, defense strategies, and future research opportunities. We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids. The review particularly includes an extensive analysis of coordinated attacks that incorporate multiple attack strategies and exploit vulnerabilities across various smart grid components to increase their adverse impact, demonstrating the complexity and potential severity of these threats. Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, blockchain, and machine learning, discussing their advancements in counteracting evolving threats and associated research challenges. In particular, our review covers a thorough examination of widely used machine learning-based mitigation strategies, analyzing their applications and research challenges spanning across supervised, unsupervised, semi-supervised, ensemble, and reinforcement learning. Further, we outline future research directions and explore new techniques and concerns. We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques, such as large language models (LLMs), and the emerging threat of adversarial machine learning in the future of smart grid security.