491 citations · 632 across the 24 of their papers we have counts for
9 papers · 1 filter
Taming Data Challenges in ML-based Security Tasks Using Generative AI
Shravya Kanchi, Neal Mangaokar, Aravind Cheruvu +4
Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmic advancements. We argue that data cha…
Harmful Terms and Where to Find Them: Measuring and Modeling Unfavorable Financial Terms and Conditions in Shopping Websites at Scale
Elisa Tsai, Neal Mangaokar, Boyuan Zheng +2
Terms and conditions for online shopping websites often contain terms that can have significant financial consequences for customers. Despite their impact, there is currently no co…
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…
Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks
Ryan Feng, Ashish Hooda, Neal Mangaokar +3
Recent work has proposed stateful defense models (SDMs) as a compelling strategy to defend against a black-box attacker who only has query access to the model, as is common for onl…
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
Ryan Feng, Neal Mangaokar, Jiefeng Chen +3
This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the r…
Physical Adversarial Examples for Object Detectors
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attack…
Tyche: Risk-Based Permissions for Smart Home Platforms
Amir Rahmati, Earlence Fernandes, Kevin Eykholt +1
Emerging smart home platforms, which interface with a variety of physical devices and support third-party application development, currently use permission models inspired by smart…
Robust Physical-World Attacks on Deep Learning Models
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes +6
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. G…
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
Earlence Fernandes, Amir Rahmati, Kevin Eykholt +1
The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. Building security into…