Large Language Models Are Unreliable for Cyber Threat Intelligence

Abstract

Several recent works have argued that Large Language Models (LLMs) can be used to tame the data deluge in the cybersecurity field, by improving the automation of Cyber Threat Intelligence (CTI) tasks. This work presents an evaluation methodology that other than allowing to test LLMs on CTI tasks when using zero-shot learning, few-shot learning and fine-tuning, also allows to quantify their consistency and their confidence level. We run experiments with three state-of-the-art LLMs and a dataset of 350 threat intelligence reports and present new evidence of potential security risks in relying on LLMs for CTI. We show how LLMs cannot guarantee sufficient performance on real-size reports while also being inconsistent and overconfident. Few-shot learning and fine-tuning only partially improve the results, thus posing doubts about the possibility of using LLMs for CTI scenarios, where labelled datasets are lacking and where confidence is a fundamental factor.

References in corpus (6)

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• "False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing
• STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports
• Software Updates Strategies: a Quantitative Evaluation against Advanced Persistent Threats
• Why, How and Where of Delays in Software Security Patch Management: An Empirical Investigation in the Healthcare Sector