Kunyang Li

Ph.D. Student, Computer Science

I am a Ph.D. student in the Computer Sciences Department at the University of Wisconsin-Madison, advised by Prof. Patrick McDaniel, and a research assistant in the Security and Privacy Research Group. I am grateful to have been mentored by Dr. Mantas Mazeika and Dr. Dan Hendrycks at CAIS. I received my B.S. in Computer Science and Mathematics (Honors, advised by Adwait Nadkarni) from William & Mary.

I am interested in building trustworthy AI systems that remain resilient in complex, adversarial environments. Specifically, I have studied how model architectures and training strategies affect adversarial robustness. I have also worked on AI functional wellbeing and its implications for safety. My current research centers on the security properties of multi-agent systems. As AI agents become more capable and collaborative, their interactions create new, emergent vulnerabilities. My goal is to develop methods and frameworks to ensure these systems are reliable and secure in real-world deployments. I am always open to discussing new ideas, feel free to reach out!

In my free time, I enjoy being outdoors—biking 🚴, hiking 🏔️, and snowboarding 🏂—as well as playing table tennis 🏓.

Recent Highlights

Publications

Report AI Wellbeing: Measuring and Improving the Functional Pleasure and Pain of AIs website paper code

Richard Ren*, Kunyang Li*, Mantas Mazeika*, Wenyu Zhang, Yury Orlovskiy, Rishub Tamirisa, Wenjie Jacky Mo, Judy Nguyen, Long Phan, Steven Basart, Austin Meek, Aditya Mehta, Oliver Ingebretsen, Alice Blair, Brianna Adewinmbi, Alice Gatti, Adam Khoja, Jason Hausenloy, Devin Kim, Dan Hendrycks

Technical Report, 2026

Conference On the Robustness Tradeoff in Fine-Tuning paper code poster

Kunyang Li, Jean-Charles Noirot Ferrand, Ryan Sheatsley, Blaine Hoak, Yohan Beugin, Eric Pauley, Patrick McDaniel

IEEE/CVF International Conference on Computer Vision (ICCV), 2025

Workshop Alignment and Adversarial Robustness: Are More Human-Like Models More Secure? paper code

Blaine Hoak*, Kunyang Li*, Patrick McDaniel

The European Conference on Artificial Intelligence (ECAI) - Workshop on Security and Privacy-Preserving AI/ML (SPAIML), 2025

Preprint Robustness Under Texture Transformations: Exploiting Natural Texture Backdoors in Vision Models paper

Blaine Hoak, Kunyang Li, Kyle Domico, Patrick McDaniel

In submission, 2025

Thesis On Adversarial and Common Robustness of Parameter-Efficient Fine-Tuning Strategies paper

Kunyang Li, Patrick McDaniel

Master Thesis, 2024

Workshop ParTEETor: A System for Partial Deployments of TEEs within Tor paper

Rachel King, Quinn Burke, Yohan Beugin, Blaine Hoak, Kunyang Li, Eric Pauley, Ryan Sheatsley, Patrick McDaniel

Workshop on Privacy in the Electronic Society (WPES), 2024

Workshop The Efficacy of Transformer-based Adversarial Attacks in Security Domains paper

Kunyang Li, Kyle Domico, Jean-Charles Noirot Ferrand, Patrick McDaniel

IEEE Conference on Military Communications (MILCOM) - AI for Cyber Workshop, 2023

Conference The Trade-off between Label Efficiency and Universality of Representations from Contrastive Learning paper

Zhenmei Shi*, Jiefeng Chen*, Kunyang Li, Jayaram Raghuram, Xi Wu, Yingyu Liang, Somesh Jha

International Conference on Learning Representations (ICLR), 2023 Spotlight (Acceptance Rate: 7.95%)

Thesis Static and Dynamic Analysis in Cryptographic-API Misuse Detection of Mobile Application paper

Kunyang Li

Undergraduate Honors Thesis, 2022

Professional Activities

Reviewer

Artifact Reviewer

IEEE S&P 2026 · NDSS 2026 · USENIX Security 2026, 2025

External Reviewer

IEEE S&P 2025 · ICLR 2025 · ACM CCS 2024 · USENIX Security 2023

Talks, Services & Outreach