activity
20242026
collaborators

7 papers

cs.CR2026

GrayShield: Bit-Level Sanitization for Transformer Model Supply-Chain Security

Armstrong Foundjem, Tsung-Hsien Chuang, Foutse Khomh +1

Transformer models such as BERT and Vision Transformer~(ViT) achieve strong performance via densely parameterized attention backbones. However, the least significant bits~(LSBs) of…

cs.SE2026

Quantifying Overclaiming Propensity in Frontier LLM Agents

Nolan Smyth, Yorguin-Jose Mantilla-Ramos, Pascal Jr Tikeng Notsawo +6

Frontier coding agents are increasingly trusted to work autonomously for long periods of time, yet what they actually did is often hard to tell from their final response. We quanti…

cs.LG2026

Efficient Safety Alignment of Language Models via Latent Personality Traits

Mohamed Amine Merzouk, Nolan Smyth, Damiano Fornasiere +3

Current safety methods for large language models are known to be vulnerable to adversarial attacks, motivating research into robust alternatives. Latent Adversarial Training (LAT)…

cs.CL2026

How Much is Left? LLMs Linearly Encode Their Remaining Output Length

Mohamed Amine Merzouk, Dmitri Carpov, Mirko Bronzi +2

Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in predictable token counts, re…

cs.AI2026

Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms

Linh Le, David Williams-King, Mohamed Amine Merzouk +2

Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to hundreds of thousands of examples), yet remain vulnerab…

cs.AI2025

Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs

Mina Taraghi, Yann Pequignot, Amin Nikanjam +2

Organizations increasingly adapt Large Language Models (LLMs) from public repositories such as HuggingFace to downstream tasks. Prior work shows that even fine-tuning on benign dat…

cs.CR2024

Diffusion-based Adversarial Purification for Intrusion Detection

Mohamed Amine Merzouk, Erwan Beurier, Reda Yaich +2

The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples pre…