38 citations · 72 across the 29 of their papers we have counts for
30 papers
AutoMark: Enabling Autoresearch to Discover Better LLM Watermarks
Thibaud Gloaguen, Robin Staab, Martin Vechev
With LLM watermarking being deployed commercially and now required by regulations, improving its reliability and effectiveness has become crucial. Yet, recent progress in the field…
Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts
Melanie Rieff, Robin Staab, Thibaud Gloaguen +2
Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking. Yet, mos…
Making Open-Source Text LLM Watermarks Durable Against Merging
Luisa Scharff, Thibaud Gloaguen, Robin Staab +1
Open-source LLMs (OSMs)arereaching near state-of-the-art performance, prompting prior works to trace the text they generate by embedding text watermarking algorithms directly into…
Widening the Gap: Exploiting LLM Quantization via Outlier Injection
Xiaohua Zhan, Kazuki Egashira, Robin Staab +2
LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risks: an adversary may release a…
Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark
Thibaud Gloaguen, Robin Staab, Mark Vero +1
With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or ti…
Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR
Kazuki Egashira, Mark Vero, Jasper Dekoninck +3
Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designe…
A Unified Framework for LLM Watermarks
Thibaud Gloaguen, Robin Staab, Nikola Jovanović +1
LLM watermarks allow tracing AI-generated texts by inserting a detectable signal into their generated content. Recent works have proposed a wide range of watermarking algorithms, e…
Pay Attention to the Triggers: Constructing Backdoors That Survive Distillation
Giovanni De Muri, Mark Vero, Robin Staab +1
LLMs are often used by downstream users as teacher models for knowledge distillation, compressing their capabilities into memory-efficient models. However, as these teacher models…
Adaptive Generation of Bias-Eliciting Questions for LLMs
Robin Staab, Jasper Dekoninck, Maximilian Baader +1
Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide. Despite their widespread adoption, growing relia…
Fewer Weights, More Problems: A Practical Attack on LLM Pruning
Kazuki Egashira, Robin Staab, Thibaud Gloaguen +2
Model pruning, i.e., removing a subset of model weights, has become a prominent approach to reducing the memory footprint of large language models (LLMs) during inference. Notably,…
Watermarking Diffusion Language Models
Thibaud Gloaguen, Robin Staab, Nikola Jovanović +1
We introduce the first watermark tailored for diffusion language models (DLMs), an emergent LLM paradigm able to generate tokens in arbitrary order, in contrast to standard autoreg…
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanović, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…
MixAT: Combining Continuous and Discrete Adversarial Training for LLMs
Csaba Dékány, Stefan Balauca, Robin Staab +2
Despite recent efforts in Large Language Model (LLM) safety and alignment, current adversarial attacks on frontier LLMs can still consistently force harmful generations. Although a…
LLM Fingerprinting via Semantically Conditioned Watermarks
Thibaud Gloaguen, Robin Staab, Nikola Jovanović +1
Most LLM fingerprinting methods teach the model to respond to a few fixed queries with predefined atypical responses (keys). This memorization often does not survive common deploym…
Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
Thibaud Gloaguen, Mark Vero, Robin Staab +1
Finetuning open-weight Large Language Models (LLMs) is standard practice for achieving task-specific performance improvements. Until now, finetuning has been regarded as a controll…
Towards Watermarking of Open-Source LLMs
Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1
While watermarks for closed LLMs have matured and have been included in large-scale deployments, these methods are not applicable to open-source models, which allow users full cont…
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act
Philipp Guldimann, Alexander Spiridonov, Robin Staab +9
The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development, but lacks clear technical interpretation, making it difficult to assess mode…
Ward: Provable RAG Dataset Inference via LLM Watermarks
Nikola Jovanović, Robin Staab, Maximilian Baader +1
RAG enables LLMs to easily incorporate external data, raising concerns for data owners regarding unauthorized usage of their content. The challenge of detecting such unauthorized u…
Discovering Spoofing Attempts on Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1
LLM watermarks stand out as a promising way to attribute ownership of LLM-generated text. One threat to watermark credibility comes from spoofing attacks, where an unauthorized thi…
A Synthetic Dataset for Personal Attribute Inference
Hanna Yukhymenko, Robin Staab, Mark Vero +1
Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide. However, their strong capabilities and vast world knowled…
Back to the Drawing Board for Fair Representation Learning
Angéline Pouget, Nikola Jovanović, Mark Vero +2
The goal of Fair Representation Learning (FRL) is to mitigate biases in machine learning models by learning data representations that enable high accuracy on downstream tasks while…
Black-Box Detection of Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab +1
Watermarking has emerged as a promising way to detect LLM-generated text, by augmenting LLM generations with later detectable signals. Recent work has proposed multiple families of…
Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab +2
Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware…
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe, Mark Vero, Robin Staab +1
As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus. While LLM privacy research has pr…