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Kanghua Mo

2 accepted papers

2025

Attractive Metadata Attack: Inducing LLM Agents to Invoke Malicious Tools

NeurIPS 2025poster

Large language model (LLM) agents have demonstrated remarkable capabilities in complex reasoning and decision-making by leveraging external tools. However, this tool-centric paradigm introduces a previously underexplored attack surface, where adversaries can manipulate tool metadata---such as names,…

Cited by 0Source ↗code ↗Scholar ↗
2025

Distraction is All You Need for Multimodal Large Language Model Jailbreaking

CVPR 2025highlight

Multimodal Large Language Models (MLLMs) bridge the gap between visual and textual data, enabling a range of advanced applications. However, complex internal interactions among visual elements and their alignment with text can introduce vulnerabilities, which may be exploited to bypass safety mechan…

Cited by 1Source ↗PDF ↗Scholar ↗