Sen Su

This survey systematically reviews resource consumption threats in large language models (LLMs) to provide a unified view of the problem landscape, from threat induction to mitigation.

This paper investigates a novel security vulnerability where imperceptible branding hints can be injected into images and subsequently re-rendered onto new objects by generative AI models, proposing both attack scenarios and a robust mitigation solution.

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and environment.