CTIBench: Evaluating LLMs in Cyber Threat Intelligence with Nidhi Rastogi - #729
Analysis
This article from Practical AI discusses CTIBench, a benchmark for evaluating Large Language Models (LLMs) in Cyber Threat Intelligence (CTI). It features an interview with Nidhi Rastogi, an assistant professor at Rochester Institute of Technology. The discussion covers the evolution of AI in cybersecurity, the advantages and challenges of using LLMs in CTI, and the importance of techniques like Retrieval-Augmented Generation (RAG). The article highlights the process of building the benchmark, the tasks it covers, and key findings from benchmarking various LLMs. It also touches upon future research directions, including mitigation techniques, concept drift monitoring, and explainability improvements.
Key Takeaways
- •CTIBench is a benchmark for evaluating LLMs in Cyber Threat Intelligence.
- •RAG is crucial for keeping LLMs up-to-date with emerging threats.
- •The research lab is focusing on mitigation techniques, concept drift monitoring, and explainability.
“Nidhi shares the importance of benchmarks in exposing model limitations and blind spots, the challenges of large-scale benchmarking, and the future directions of her AI4Sec Research Lab.”