SRI Lab group photo SRI Lab group photo in color
The Secure, Reliable, and Intelligent Systems (SRI) Lab is a research group in the Department of Computer Science at ETH Zurich. Our research focuses on reliable, secure, and trustworthy machine learning, with emphasis on large language models. We currently study issues of controllability, security and privacy, and reliable evaluation of LLMs, their application to mathematical reasoning and coding, as well as generative AI watermarking, AI regulations, federated learning privacy, robustness and fairness certification, and quantum computing. Our work has led to six ETH spin-offs: NetFabric (AI for systems), LogicStar (AI code agents), LatticeFlow (robust ML), InvariantLabs (secure AI agents; acquired), DeepCode (AI for code; acquired), and ChainSecurity (security verification; acquired). To learn more about our work see our Research page, recent Publications, and GitHub releases. To stay up to date follow our group on Twitter.

Latest News

10.10.2026

Mark Vero was interviewed by WIRED about using LLM-powered honeypots to keep attackers occupied. The article features our Honeyval research on evaluating these systems against AI hacking agents.

07.10.2026

Martin Vechev was selected by OpenAI to test and evaluate its new watermarking technique for AI-generated text. He joins a group of researchers studying the method’s capabilities and limitations. Read the ETH announcement.

10.09.2026

ETH Zurich profiled Mark Vero and his research on LLM-powered honeypots and protection against manipulated AI models. The interview also follows his path from Budapest to doctoral research at SRI Lab.

Most Recent Publications

Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
Mark Vero, Fabian Kaczmarczyck, Ivan Petrov, Ilia Shumailov, Jamie Hayes, Niels Heinen, Tianqi Fan, Luca Invernizzi, Martin Vechev
NeurIPS E&D 2026
Making Open-Source Text LLM Watermarks Durable Against Merging
Luisa Scharff, Thibaud Gloaguen, Robin Staab, Martin Vechev
NeurIPS 2026
Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark
Thibaud Gloaguen, Robin Staab, Mark Vero, Martin Vechev
NeurIPS 2026
Coding Agents Don't Know When to Act
Thibaud Gloaguen, Niels Mündler, Mark Niklas Mueller, Veselin Raychev, Martin Vechev
COLM 2026

Latest Blog Posts

Probing the SynthID-text deployment on Gemini APIs

Google DeepMind's SynthID-Text is the first large-scale LLM watermark deployment, yet little is known about how it is actually deployed. Using our work, we, for the first time, identify several key watermark hyperparameters. We find that: (1) all Gemini models are watermarked and they all share the same key except...

Searching for errors in mathematical papers with GPT-6.1 Sol

We use GPT-6.1 Sol to search for counterexamples to main theorems in recent arXiv papers and flag major errors in 6% of papers published before August 2026. The reported rate drops to 1.5% in August and September, though the cause of this decline remains unclear.