activity
20242026
most citedPII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMs

1 citations · 2 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CL2026

MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval

Meriem Yacoubi, Pia Schmidt, Nenad Petrovic +3

Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved contradictions. Existing textu…

cs.CL2026

Jais 2: A Family of Arabic-Centric Open Large Language Models

Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim +57

Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong p…

cs.LG2025

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…

cs.LG2025

One-Class Domain Adaptation via Meta-Learning

Stephanie Holly, Thomas Bierweiler, Stefan von Dosky +3

The deployment of IoT (Internet of Things) sensor-based machine learning models in industrial systems for anomaly classification tasks poses significant challenges due to distribut…

cs.CL2024★ 1 cited

PII-Scope: A Comprehensive Study on Training Data PII Extraction Attacks in LLMs

Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendes +2

In this work, we introduce PII-Scope, a comprehensive benchmark designed to evaluate state-of-the-art methodologies for PII extraction attacks targeting LLMs across diverse threat…

cs.CR2024

ObfuscaTune: Obfuscated Offsite Fine-tuning and Inference of Proprietary LLMs on Private Datasets

Ahmed Frikha, Nassim Walha, Ricardo Mendes +3

This work addresses the timely yet underexplored problem of performing inference and finetuning of a proprietary LLM owned by a model provider entity on the confidential/private da…