activity
20202026
most citedStateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks

15 citations · 32 across the 17 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks

Zi Wang, Divyam Anshumaan, Ashish Hooda +2

Optimization methods are widely employed in deep learning to identify and mitigate undesired model responses. While gradient-based techniques have proven effective for image models…

cs.CR2024★ 1 cited

PolicyLR: A Logic Representation For Privacy Policies

Ashish Hooda, Rishabh Khandelwal, Prasad Chalasani +2

Privacy policies are crucial in the online ecosystem, defining how services handle user data and adhere to regulations such as GDPR and CCPA. However, their complexity and frequent…

cs.CV2024

CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems

Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed +3

Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential. Standard ev…

cs.CR2024

PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails

Neal Mangaokar, Ashish Hooda, Jihye Choi +4

Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks th…

cs.SE2024★ 3 cited

Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates

Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis +3

Large Language Models' success on text generation has also made them better at code generation and coding tasks. While a lot of work has demonstrated their remarkable performance o…