7 citations · 13 across the 18 of their papers we have counts for
4 papers · 1 filter
NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability
Krishna Kanth Nakka, Alexandre Alahi
The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single m…
PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage
Krishna Kanth Nakka, Xue Jiang, Dmitrii Usynin +1
This paper investigates privacy jailbreaking in LLMs via steering, focusing on whether manipulating activations can bypass LLM alignment and alter response behaviors to privacy rel…
Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoders
Krishna Kanth Nakka
Interpretability is critical in high-stakes domains such as medical imaging, where understanding model decisions is essential for clinical adoption. In this work, we introduce Spar…
PrivacyScalpel: Enhancing LLM Privacy via Interpretable Feature Intervention with Sparse Autoencoders
Ahmed Frikha, Muhammad Reza Ar Razi, Krishna Kanth Nakka +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing but also pose significant privacy risks by memorizing and leaking Personally I…