2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents
Lucas Biechy, Cédric Eichler, Héber H. Arcolezi +1
While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice of information sources. We intro…
Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
Yanming Li, Cédric Eichler, Nicolas Anciaux +3
We propose a system for marking sensitive or copyrighted texts to detect their use in fine-tuning large language models under black-box access with statistical guarantees. Our meth…
LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs
Luis Ibanez-Lissen, Lorena Gonzalez-Manzano, Jose Maria de Fuentes +1
Large Language Models (LLMs) are being extensively used for cybersecurity purposes. One of them is the detection of vulnerable codes. For the sake of efficiency and effectiveness,…
LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states
Luis Ibanez-Lissen, Lorena Gonzalez-Manzano, Jose Maria de Fuentes +2
Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Previous efforts focus on black-to…
Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction
Cédric Eichler, Nathan Champeil, Nicolas Anciaux +3
The rise of Large Language Models (LLMs) has triggered legal and ethical concerns, especially regarding the unauthorized use of copyrighted materials in their training datasets. Th…