HICSS-59 Minitrack CFP: Collaborative AI, LLMs, & Cybersecurity - Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI (AI4Cyber and Cyber4AI 2026)
Track: Collaboration Systems and Technologies
Conference: Hawaii International Conference on System Sciences (HICSS-59)
Location: Hyatt Regency Maui, Maui, Hawaii
Conference Website: https://hicss.hawaii.edu
Conference Dates: January 6–9, 2026
Track Description: https://hicss.hawaii.edu/tracks-59/collaboration-systems-and-technologies/#cybersecurity-in-the-age-of-artificial-intelligence-ai-for-cybersecurity-and-cybersecurity-for-ai-minitrack

=====Dates (11:59 pm Hawaii Standard Time)=====
Submission Deadline: June 15, 2025
Decision Notification: August 17, 2025
Final Manuscript Deadline: September 22, 2025
Registration Deadline: October 1, 2025

=====Mini-Track Introduction=====
Cybersecurity and Artificial Intelligence (AI) are key domains whose intersection holds great promises and poses significant threats. The nature of AI and Cybersecurity encompasses many domains. While some perspectives are narrowly focused (e.g., point solutions inside an organization identifying threats in a network stream), many are very sweeping and are either collaborative or tackle collaborative domains (e.g., identifying intentional or unintentional cybersecurity threats propagating across collaboration platforms).

Implementing AI and Cybersecurity can also be internal to an organization or broadly collaborative (e.g., organizations working and competing together in adversarial AI research). Conversely, cybersecurity for AI has point solutions internal to organizations and broadly collaborative domains (e.g., collaboratively protecting from adversarial examples in shared data sets or shared models with multi-organizational transfer learning). However, the range and scope of how AI could be used for cybersecurity and how to improve the cybersecurity of AI remain relatively understudied yet critically important areas.

=====Topics=====
This minitrack focuses on AI and Cybersecurity that works in broader domains, collaborative inter-organizational realms, shared collaborative domains, or with collaborative technologies. The threats being addressed with and/or to AI are intended to be sweeping and have significant societal impact. Broadly, the topics and research areas include, but are not limited to:
+ Novel applications of Artificial Intelligence, Machine Learning, and Deep Learning in Cybersecurity as it pertains to multi-user/multi-organizational collaborative domains and/or systems
+ Adversarial AI/Machine Learning Applications in Cybersecurity that collaboratively span organizations or apply to collaborative systems (i.e., malware, phishing, or any applicable threat/identification domain)
+ Protecting AI that is used collaboratively (i.e., shared data sets, shared models, shared applications) or spans collaborative domains from cybersecurity threats (i.e., adversarial examples, trojans, model inversion
+ Using AI to protect AI in any appropriate wide-reaching setting
+ Novel Collaboration approaches to leveraging and protecting AI in the cybersecurity domain
+ Sharing/disseminating tools, techniques, and applications of AI in Cybersecurity and Cybersecurity for AI that apply to the overarching theme of this minitrack

===Examples===
+ Modern LLMs: Results Integrity, Prompt Security, Prompt Attack Detection, Result Error Detection, Dangerous Output Detection, Hallucination Detection, Prompt Jailbreaking.
+ Cybersecurity Domain Data Analytics: Leveraging AI to analyze any of the myriad datasets in the cybersecurity domain such as log files, network traffic, data at rest, etc. for legitimate cybersecurity purposes.
+ Vulnerability Assessment: Scanning Code for Vulnerabilities using AI / LLMS; Tracking and identifying / labeling code, containers, or repositories based on their vulnerabilities and/or vulnerability persistence over time and forks.
+ Secure Coding: Securing existing code or automatically generating new secure code either from scratch or by generating secure code clones.
+ Remediation: Effectively and efficiently identifying appropriate remediations for detected vulnerabilities from the large amounts of existing data.
+ Model Security for AI and LLM Models: Identifying models that have been perturbed, perturbing models to create model perturbation detection technologies, detecting the effect of model perturbations, identifying bias in models, identifying errors in models, removing perturbations from models.
+ Security for AI and LLM Datasets: Insuring distributed dataset integrity, detecting perturbations in datasets, identifying the effects of dataset perturbations, removing perturbations from datasets.
+ Attack Detection: Analyzing real-time data streams to identify immediate attacks as they occur.

=====Co-Chairs Information=====
Mark Patton (Primary Contact)
University of Arizona
mpatton@email.arizona.edu
o: 520-626-8614
m: 520-250-4763

Sagar Samtani
Indiana University
ssamtani@iu.edu

Hongyi Zhu
University of Texas at San Antonio
hongyi.zhu@utsa.edu

Hsinchun Chen
University of Arizona
hsinchun@email.arizona.edu