Haoxuan Qu

The paper develops a novel attack method for multi-agent discussions under continuous monitoring, demonstrating that monitoring alone is insufficient to secure these systems.

This paper introduces the 'wide-net-casting' jailbreak scenario, demonstrating that querying a group of large language models can expose significant, previously overlooked safety risks, with a novel method achieving 100% jailbreak success in some tests.

The paper introduces ToolFG, a novel tool-integrated MLLM framework that enhances fine-grained image classification by enabling models to autonomously use external tools to gather verifiable visual cues.