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

14.07.2025

SRI Lab is presenting 14 works at ICML 2025 in Vancouver: 9 at the main conference and 5 at workshops. See the twitter thread for more details.

25.06.2025

Our ETH spin-off Invariant Labs was acquired by Snyk. See the article on the D-INFK news channel.

24.04.2025

SRI Lab is presenting 14 works at ICLR 2025 in Singapore: 5 at the main conference and 9 more at workshops. See the twitter thread for more details.

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Most Recent Publications

Watermarking Autoregressive Image Generation
Nikola Jovanović, Ismail Labiad, Tomáš Souček, Martin Vechev, Pierre Fernandez
NeurIPS 2025

Paper
Code
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
Mislav Balunović, Jasper Dekoninck, Nikola Jovanović, Ivo Petrov, Martin Vechev
NeurIPS Datasets and Benchmarks 2025

Website
Paper
Code
qblaze: An Efficient and Scalable Sparse Quantum Simulator
Hristo Venev, Thien Udomsrirungruang, Dimitar Dimitrov, Timon Gehr, Martin Vechev
ACM OOPSLA 2025

Paper
SoK: Data Minimization in Machine Learning
Robin Staab*, Nikola Jovanović*, Kimberly Mai, Prakhar Ganesh, Martin Vechev, Ferdinando Fioretto, Matthew Jagielski
arXiv 2025 * Equal contribution

Paper
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