Oregon State University
Computer Science Dept.
Cybersecurity | AI

Contact Information

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Office: Room 2029, Kelley Engineering Center (KEC)
2500 NW Monroe Ave
Corvallis, OR 97331 USA
Office Hours: Tu/Th: 2 - 3 pm

          

Press

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06.2023

OSU AI News

04.2022

TechXplore
Techradar.pro

06.2021

TechTalks

05.2021

Dev Podcast
MIT Tech Review

02.2021

USENIX Enigma 2021
(Ted Talk for Security)

Teaching

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Grad.	AI579: Trustworthy ML
Grad.	CS578: Cyber-Sec.
UGrad.	CS370: Intro to Sec.
UGrad.	CS344: OS I

Students [Full list]

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Zachary Coalson (PhD, CS)
Derek Lilienthal (PhD, AI)
Jose Escamilla (PhD, CS
  co-advise w. Huazheng Wang)

Eunjin Roh (MS, CS)
Gabriel Ritter (MS, CS
  co-advise w. Rakesh Bobba)

Leo Marchyok (BS, CS)
AJ (BS, CS)
Dongwoo Kang (BS, CS)
Aiden Gabriel (BS, CS)
Nyx (CS)

Alumni

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'25: Tahmid Prato (MS, CS)
'24: Lucas Ball (MS, CS)
'24: Jonathan Keller (MS, CS)
'24: Ramya Jayaraman (MS, AI)
'23: Hoang Le (MS, CS)

'24: Colin Pannikkat (BS, CS)
'24: Evan Mrazik (BS, CS)
'22: Peter M-Stevens (BS, CS)
'22: Ryan Little (BS, CS)
  Now a PhD student at UMD

I am a computer scientist and educator (and also an AI hacker) dedicated to addressing threats to the trustworthiness and social responsibility of AI-enabled systems while fostering the development of the next-generation workforce capable of auditing, characterizing, and countering these threats. I received the Google Faculty Research Award 2023 and the Samsung Global Research (GRO) Award 2023, 2022. I am selected as a DARPA Riser (2022) and was invited as a speaker at USENIX Enigma (2021).

Please read this page and fill out this form if you're motivated to work with me.

Bio

I am currently an Assistant Professor of Computer Science at Oregon State University. I earned my Ph.D. from the University of Maryland, College Park, under the supervision of Prof. Tudor Dumitras in 2021. I received my bachelor's degree from Seoul National University in 2015. I was fortunate to spend a winter at Google Brain in 2021 (working with Dr. Nicholas Carlini and Dr. Alexey Kurakin) and to spend 6-months at Frame.io in 2017 (working with Dr. Abhinav Srivastava).

News

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Before Dec. 2023  

Selected Publications [Full list]

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SoK: Watermarking for AI-Generated Content
Xuandong Zhao, Sam Gunn, Miranda Christ, Jaiden Fairoze, Andres Fabrega, Nicholas Carlini, Milad Nasr, Sanghyun Hong, Florian Tramer, Sanjam Garg, Somesh Jha, Lei Li, Yu-Xiang Wang, and Dawn Song
The 46th IEEE Symposium on Security and Privacy (IEEE S&P). 2025.
PDF

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, and Nicholas Carlini
Advances in Neural Information Processing Systems (NeurIPS). 2024.
PDF | Code | Poster

Parameterized Physics-informed Neural Networks for Parameterized PDEs
Woojin Cho, Minju Jo, Haksoo Lim, Kookjin Lee, Dongeun Lee, Sanghyun Hong, and Noseong Park
International Conference on Machine Learning (ICML). 2024. [Oral]
PDF | Code | Poster

Handcrafted Backdoors in Deep Neural Networks
Sanghyun Hong, Nicholas Carlini, and Alexey Kurakin
Advances in Neural Information Processing Systems (NeurIPS). 2022. [Oral]
PDF | Code & Supp. | Talk (on NeurIPS'22 Website)

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong,
Nicholas Carlini (*authors ordered reverse-alphabetically)
The ACM Conference on Computer and Communications Security (CCS), 2022.
PDF | Code | Media

Data Poisoning Won't Save You From Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramer
International Conference on Learning Representations (ICLR) 2022.
PDF | Code | Poster

Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes
Sanghyun Hong, Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, and Tudor Dumitraș
Advances in Neural Information Processing Systems (NeurIPS) 2021.
PDF | Code | Poster

A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
*Sanghyun Hong, *Yigitcan Kaya, Ionuţ-Vlad Modoranu, and Tudor Dumitraș (*equal contribution)
International Conference on Learning Representations (ICLR) 2021. [Spotlight]
PDF | Code | Spotlight Presentation

Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks
Under Hardware Fault Attacks
Sanghyun Hong, Pietro Frigo, Yigitcan Kaya, Cristiano Giuffrida, and Tudor Dumitraș
Proceedings of The 28th USENIX Security Symposium (USENIX Security) 2019.
PDF | Presentation

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
Yigitcan Kaya, Sanghyun Hong, and Tudor Dumitraș
International Conference on Machine Learning (ICML) 2019.
PDF | Code