⚠️ Asking to get roasted 🚨 well, sorta
Please tell me why you hate AI.
Cosmic brownie points if it's not because of the environment, data privacy, or copyright. Those are all super valid reasons, but I've heard them before.
I'm looking for haters, please step up to the plate.
Get vicious, show me the hate.
@dnsprincess Large language models are an interesting technology that deserves academic research.
As a "product" it just doesn't work. All the promise of what these glorified chatbots could do has not materialized - and as study after study shows - it's not getting closer ("newer models tended to perform worse in generalization accuracy than earlier ones." - https://royalsocietypublishing.org/doi/10.1098/rsos.241776 ; other similar claims of RLHF reducing the quality of the outputs abound).
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.
alissa
mau 🏳️🌈#EndFossilFuels
DFIR Notes