Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars. - Mark Martinez; Chawin Sitawarin; Kevin Finch; Lennart Meincke; Alex Yablonski; Alain L. Kornhauser (CoRR, 2017)

Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. - Vikash Sehwag; Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Mung Chiang; Prateek Mittal (CCS, 2018)

Enhancing robustness of machine learning systems via data transformations. - Arjun Nitin Bhagoji; Daniel Cullina; Chawin Sitawarin; Prateek Mittal (CISS, 2018)

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos. - Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Prateek Mittal; Mung Chiang (CoRR, 2018)

DARTS: Deceiving Autonomous Cars with Toxic Signs. - Chawin Sitawarin; Arjun Nitin Bhagoji; Arsalan Mosenia; Mung Chiang; Prateek Mittal (CoRR, 2018)

Analyzing the Robustness of Open-World Machine Learning. - Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal (AISec@CCS, 2019)

On the Robustness of Deep K-Nearest Neighbors. - Chawin Sitawarin; David A. Wagner 001 (IEEE Symposium on Security and Privacy Workshops, 2019)

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. - Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal (CoRR, 2019)

Minimum-Norm Adversarial Examples on KNN and KNN based Models. - Chawin Sitawarin; David A. Wagner 001 (SP (Workshops), 2020)

Improving Adversarial Robustness Through Progressive Hardening. - Chawin Sitawarin; Supriyo Chakraborty; David A. Wagner 001 (CoRR, 2020)

SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing. - Chawin Sitawarin; Supriyo Chakraborty; David A. Wagner 001 (AISec@CCS, 2021)

Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams. - Chawin Sitawarin; Evgenios M. Kornaropoulos; Dawn Song; David A. Wagner 001 (NeurIPS, 2021)

Demystifying the Adversarial Robustness of Random Transformation Defenses. - Chawin Sitawarin; Zachary J. Golan-Strieb; David A. Wagner 001 (ICML, 2022)

REAP: A Large-Scale Realistic Adversarial Patch Benchmark. - Nabeel Hingun; Chawin Sitawarin; Jerry Li; David A. Wagner 001 (ICCV, 2023)

Part-Based Models Improve Adversarial Robustness. - Chawin Sitawarin; Kornrapat Pongmala; Yizheng Chen 001; Nicholas Carlini; David A. Wagner 001 (ICLR, 2023)

Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. - Chawin Sitawarin; Florian Tramèr; Nicholas Carlini (ICML, 2023)

Short: Certifiably Robust Perception Against Adversarial Patch Attacks: A Survey. - Chong Xiang 001; Chawin Sitawarin; Tong Wu; Prateek Mittal (VehicleSec, 2023)

OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. - Lin Li 070; Yifei Wang; Chawin Sitawarin; Michael W. Spratling (CoRR, 2023)

Defending Against Transfer Attacks From Public Models. - Chawin Sitawarin; Jaewon Chang; David Huang; Wesson Altoyan; David A. Wagner 001 (CoRR, 2023)

Mark My Words: Analyzing and Evaluating Language Model Watermarks. - Julien Piet; Chawin Sitawarin; Vivian Fang; Norman Mu; David A. Wagner 001 (CoRR, 2023)

Jatmo: Prompt Injection Defense by Task-Specific Finetuning. - Julien Piet; Maha Alrashed; Chawin Sitawarin; Sizhe Chen; Zeming Wei; Elizabeth Sun; Basel Alomair; David A. Wagner 001 (ESORICS (1), 2024)

SPDER: Semiperiodic Damping-Enabled Object Representation. - Kathan Shah; Chawin Sitawarin (ICLR, 2024)

PubDef: Defending Against Transfer Attacks From Public Models. - Chawin Sitawarin; Jaewon Chang; David Huang; Wesson Altoyan; David A. Wagner 001 (ICLR, 2024)

OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. - Lin Li 070; Yifei Wang; Chawin Sitawarin; Michael W. Spratling (ICML, 2024)

StruQ: Defending Against Prompt Injection with Structured Queries. - Sizhe Chen; Julien Piet; Chawin Sitawarin; David A. Wagner 001 (CoRR, 2024)

PAL: Proxy-Guided Black-Box Attack on Large Language Models. - Chawin Sitawarin; Norman Mu; David A. Wagner 001; Alexandre Araujo (CoRR, 2024)

New Perspectives on Adversarially Robust Machine Learning Systems - Chawin Sitawarin (2024)

Vulnerability Detection with Code Language Models: How Far are We? - Yangruibo Ding; Yanjun Fu; Omniyyah Ibrahim; Chawin Sitawarin; Xinyun Chen; Basel Alomair; David A. Wagner 001; Baishakhi Ray; Yizheng Chen 001 (ICSE, 2025)

Stronger Universal and Transferable Attacks by Suppressing Refusals. - David Huang; Avidan Shah; Alexandre Araujo; David A. Wagner 001; Chawin Sitawarin (NAACL (Long Papers), 2025)

MARKMyWORDS: Analyzing and Evaluating Language Model Watermarks. - Julien Piet; Chawin Sitawarin; Vivian Fang; Norman Mu; David A. Wagner 001 (SaTML, 2025)

JailbreaksOverTime: Detecting Jailbreak Attacks Under Distribution Shift. - Julien Piet; Xiao Huang; Dennis Jacob; Annabella Chow; Maha Alrashed; Geng Zhao; Zhanhao Hu; Chawin Sitawarin; Basel Alomair; David A. Wagner 001 (CoRR, 2025)

Lessons from Defending Gemini Against Indirect Prompt Injections. - Chongyang Shi 003; Sharon Lin; Shuang Song 001; Jamie Hayes; Ilia Shumailov; Itay Yona; Juliette Pluto; Aneesh Pappu; Christopher A. Choquette-Choo; Milad Nasr; Chawin Sitawarin; Gena Gibson; et al. (CoRR, 2025)

How much do language models memorize? - John X. Morris; Chawin Sitawarin; Chuan Guo 001; Narine Kokhlikyan; G. Edward Suh; Alexander M. Rush; Kamalika Chaudhuri; Saeed Mahloujifar (CoRR, 2025)

Defending Against Prompt Injection With a Few DefensiveTokens. - Sizhe Chen; Yizhu Wang; Nicholas Carlini; Chawin Sitawarin; David A. Wagner 001 (CoRR, 2025)

Does More Inference-Time Compute Really Help Robustness? - Tong Wu; Chong Xiang 001; Jiachen T. Wang; Weichen Yu; Chawin Sitawarin; Vikash Sehwag; Prateek Mittal (CoRR, 2025)