Ashish Hooda

I am a Ph.D. student at University of Wisconsin-Madison majoring in Computer Sciences. My research interests lie in the intersection of Machine Learning and Security. I work with Prof. Somesh Jha and Prof. Kassem Fawaz at MADS&P, and with Prof. Earlence Fernandes. I did my undergraduate at Indian Institute of Technology Delhi, majoring in Electrical Engineering with minor in Computer Science.

Drop me an email if you want to chat!

Recent News

• Jan 2025: Functional Homotopy: Smoothing Discrete Optimization Via Continous Parameters for LLM Jailbreak Attacks accepted at ICLR 2025! 🎉
• Nov 2024: PolicyLR: A LLM compiler for Logic based Representation for Privacy Policies accepted at NeurIPS Safe & Trustworthy Agents Workshop 2024! 🎉
• May 2024: PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails accepted at ACL 2024! 🎉
• May 2024: Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates accepted at ICML 2024! 🎉
• Nov 2023: Experimental Analyses of the Physical Surveillance Risks in Client-Side Content Scanning accepted at NDSS 2024! 🎉
• Oct 2023: D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles accepted at WACV 2024! 🎉
• Aug 2023: Presented work on attacks against Stateful Defenses at Google Research!
• Aug 2023: Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks accepted at CCS 2023! 🎉
• July 2023: Started Internship at Google Research with Mihai Christodorescu and Miltos Allamanis working on evaluating LLMs!

Work Experience

Research

	Functional Homotopy: Smoothing Discrete Optimization Via Continous Parameters for LLM Jailbreak Attacks ICLR 2025 (International Conference on Learning Representations) PAPER Zi Wang*, Divyam Anshumaan*, Ashish Hooda, Yudong Chen, Somesh Jha
	Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface IEEE S&P (IEEE Symposium on Security and Privacy) PAPER Andrey Labunets, Nishit Pandya, Ashish Hooda, Xiaohan Fu, Earlence Fernandes
	PolicyLR: A LLM compiler for Logic based Representation for Privacy Policies NeurIPS Workshop 2024 (Safe & Trustworthy Agents) PAPER Ashish Hooda, Rishabh Khandelwal, Prasad Chalasani, Kassem Fawaz, Somesh Jha
	Synthetic Counterfactual Faces Preprint PAPER Guruprasad V Ramesh, Harrison Rosenberg, Ashish Hooda, Kassem Fawaz
	PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails ACL 2024 (Association for Computational Linguistics) PAPER Neal Mangaokar*, Ashish Hooda*, Jihye Choi, Shreyas Chandrashekaran, Kassem Fawaz, Somesh Jha, Atul Prakash
	Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates ICML 2024 (International Conference on Machine Learning) PAPER Ashish Hooda, Mihai Christodorescu, Miltos Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha.
	Experimental Analyses of the Physical Surveillance Risks in Client-Side Content Scanning NDSS 2024 (Network and Distributed System Security Symposium) PAPER VIDEO Ashish Hooda, Andrey Labunets, Tadayoshi Kohno, Earlence Fernandes.
	D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles WACV 2024 (IEEE/CVF Winter Conference on Applications of Computer Vision) PAPER CODE VIDEO Ashish Hooda*, Neal Mangaokar* , Ryan Feng, Kassem Fawaz, Somesh Jha, Atul Prakash.
	Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black- box Attacks CCS 2023 (ACM Conference on Computer and Communications Security) PAPER CODE Ryan Feng*, Ashish Hooda*, Neal Mangaokar* , Kassem Fawaz, Somesh Jha, Atul Prakash.
	Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks ICML Workshop 2023 (AdvML-Frontiers'23) PAPER Ashish Hooda*, Neal Mangaokar* , Ryan Feng*, Kassem Fawaz, Somesh Jha, Atul Prakash.
	SkillFence: A Systems Approach to Mitigating Voice-Based Confusion Attacks IMWUT / UBICOMP 2022 (ACM Interactive, Mobile, Wearable and Ubiquitous Technologies) PAPER Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla, Earlence Fernandes , Kassem Fawaz.
	Invisible Perturbations: Physical Adv Examples Exploiting the Rolling Shutter Effect CVPR 2021 (Conference on Computer Vision and Pattern Recognition) PAPER CODE VIDEO Athena Sayles*, Ashish Hooda*, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes.

Talks