Lea Duesterwald (Carnegie Mellon University), Ian Yang (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

Human actions or lack thereof contribute to a large majority of cybersecurity incidents. Traditionally, when looking for advice on cybersecurity questions, people have turned to search engines or social sites like Reddit. The rapid adoption of chatbot technologies is offering a potentially more direct way of getting similar advice. Initial research suggests, however, that while chatbot answers to common cybersecurity questions tend to be fairly accurate, they may not be very effective as they often fall short on other desired qualities such as understandability, actionability, or motivational power. Research in this area thus far has been limited to the evaluation by researchers themselves on a small number of synthetic questions. This article reports on what we believe to be the first in situ evaluation of a cybersecurity Question Answering (QA) assistant. We also evaluate a prompt engineered to help the cybersecurity QA assistant generate more effective answers. The study involved a 10-day deployment of a cybersecurity QA assistant in the form of a Chrome extension. Collectively, participants (N=51) evaluated answers generated by the assistant to over 1,000 cybersecurity questions they submitted as part of their regular day-to-day activities. The results suggest that a majority of participants found the assistant useful and often took actions based on the answers they received. In particular, the study indicates that prompting successfully improved the effectiveness of answers and, in particular, the likelihood that users follow their recommendations (fraction of participants who actually followed the advice was 0.514 with prompting vs. 0.402 without prompting, p=4.61E-04), an impact on people’s actual behavior. We provide a detailed analysis of data collected in this study, discuss their implications, and outline next steps in the development and deployment of effective cybersecurity QA assistants that offer the promise of changing actual user behavior and of reducing human-related security incidents.

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Yunbo Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Yuejia Cheng (Shanghai DeCareer Consulting Co., Ltd), Kailun Wang (Beijing Jiaotong University), Xiaoguo Li (College of Computer Science, Chongqing University), Jianfei Sun (School of Computing and Information Systems, Singapore Management University), Jiachen Shen (Shanghai Key Laboratory of Trustworthy Computing, East China Normal…

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The Midas Touch: Triggering the Capability of LLMs for...

Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of…

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Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei (Northeastern University)

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BARBIE: Robust Backdoor Detection Based on Latent Separability

Hanlei Zhang (Zhejiang University), Yijie Bai (Zhejiang University), Yanjiao Chen (Zhejiang University), Zhongming Ma (Zhejiang University), Wenyuan Xu (Zhejiang University)

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