4 papers
Can LLMs Make (Personalized) Access Control Decisions?
Friederike Groschupp, Daniele Lain, Aritra Dhar +2
Precise access control decisions are crucial for the security of both traditional applications and emerging agent-based systems. Typically, these decisions are made by users during…
RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations
Aritra Dhar, Vasilije Stambolic, Lukas Cavigelli
Retrieval-Augmented Generation (RAG) increases the reliability and trustworthiness of the LLM response and reduces hallucination by eliminating the need for model retraining. It do…
AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs
Lara Magdalena Lazier, Aritra Dhar, Vasilije Stambolic +1
Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. However, as current LLMs are vulnerable to leaking sensitive informat…
Ascend-CC: Confidential Computing on Heterogeneous NPU for Emerging Generative AI Workloads
Aritra Dhar, Clément Thorens, Lara Magdalena Lazier +1
Cloud workloads have dominated generative AI based on large language models (LLM). Specialized hardware accelerators, such as GPUs, NPUs, and TPUs, play a key role in AI adoption d…