Tomer Bin

Title: Tomer Bin and Network Security for Machine Learning Models

Introduction

Tomer Bin is an inventor based in Tel Aviv, Israel, whose patent work addresses security for AI applications guided by large language models. His listed patent focuses on identifying potentially adversarial input and helping prevent it from influencing an AI application.

Latest Patents

Bin is associated with one patent, titled “Network Security for Machine Learning Models Including Large Language Models (LLMs).” The invention describes a system that monitors an interaction session with an LLM-guided application. While the model processes the session, the system traces control or data flow through the transformer’s attention heads to identify output tokens that stem from input supplied by an external resource. It can then trigger a security action intended to prevent that resource from feeding adversarial input into the application. Bin’s listed portfolio includes 1 patent.

Career Highlights

Bin is associated with Cato Networks Ltd. His patent reflects work on applying model-internal tracing to network and application security, particularly where external resources may introduce harmful inputs.

Collaborations

The provided information lists Itay Ravia and Adir Gruss as Bin’s coworkers. Their names are included here as professional associations; no specific role in the patent is stated.

Conclusion

Tomer Bin’s listed invention combines transformer-layer tracing with security actions to help protect LLM-guided applications from adversarial input originating from external resources.