9 papers
SWaRL: Safeguard Code Watermarking via Reinforcement Learning
Neusha Javidnia, Ruisi Zhang, Ashish Kundu +1
We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedding unique and verifiable signatures i…
LoRA-based Parameter-Efficient LLMs for Continuous Learning in Edge-based Malware Detection
Christian Rondanini, Barbara Carminati, Elena Ferrari +2
The proliferation of edge devices has created an urgent need for security solutions capable of detecting malware in real time while operating under strict computational and memory…
Advancing Honeywords for Real-World Authentication Security
Sudiksha Das, Ashish Kundu
Introduced by Juels and Rivest in 2013, Honeywords, which are decoy passwords stored alongside a real password, appear to be a proactive method to help detect password credentials…
Apollo: A Posteriori Label-Only Membership Inference Attack Towards Machine Unlearning
Liou Tang, James Joshi, Ashish Kundu
Machine Unlearning (MU) aims to update Machine Learning (ML) models following requests to remove training samples and their influences on a trained model efficiently without retrai…
Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Models
ÄorÄe Klisura, Joseph Khoury, Ashish Kundu +2
Access control is a cornerstone of secure computing, yet large language models often blur role boundaries by producing unrestricted responses. We study role-conditioned refusals, f…
How Good LLM-Generated Password Policies Are?
Vivek Vaidya, Aditya Patwardhan, Ashish Kundu
Generative AI technologies, particularly Large Language Models (LLMs), are rapidly being adopted across industry, academia, and government sectors, owing to their remarkable capabi…