Language Model Security and Privacy

We study the security and privacy implications of deploying large language models in real-world applications, evaluating their ability to compromise user privacy and developing algorithms that exploit attack vectors stemming from common LLM deployment practices.

Startups

Invariant Labs

Invariant Labs makes AI agents secure, reliable and robust.

Publications

2025

Black-Box Adversarial Attacks on LLM-Based Code Completion

Slobodan Jenko*, Niels Mündler*, Jingxuan He, Mark Vero, Martin Vechev

ICML 2025 * Equal contribution

Paper

Mind the Gap: A Practical Attack on GGUF Quantization

Kazuki Egashira, Robin Staab, Mark Vero, Jingxuan He, Martin Vechev

ICML 2025 BuildingTrust@ICLR25 Oral

Paper
Code

Finetuning-Activated Backdoors in LLMs

Thibaud Gloaguen, Mark Vero, Robin Staab, Martin Vechev

arXiv 2025

Paper
Code

Large Language Models are Advanced Anonymizers

Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev

ICLR 2025

Paper
Code

2024

A Synthetic Dataset for Personal Attribute Inference

Hanna Yukhymenko, Robin Staab, Mark Vero, Martin Vechev

NeurIPS Datasets and Benchmarks 2024

Paper
Code

Private Attribute Inference from Images with Vision-Language Models

Batuhan Tömekçe, Mark Vero, Robin Staab, Martin Vechev

NeurIPS 2024

Paper
Code

Exploiting LLM Quantization

Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev

NeurIPS 2024 NextGenAISafety@ICML24 Oral

Website
Paper
Code

Beyond Memorization: Violating Privacy Via Inference with Large Language Models

Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev

ICLR 2024 Spotlight, 2024 PPPM-Award

Website
Paper
Code