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20212026
most citedMisusing Tools in Large Language Models With Visual Adversarial Examples

6 citations · 8 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Visual Abstention in Unified Multimodal Models

Chufan Shi, Cheng Yang, Tiannuo Yang +4

Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We formalize visua…

cs.CL2026

Recursive Self-Improvement in Unified Multimodal Models

Huijuan Wang, Chufan Shi, Cheng Yang +3

Unified multimodal models (UMMs) understand and generate both text and images, which lets a model produce its own training data. Existing self-improvement in UMMs keeps supervision…

cs.CV2026

InfoEdit: Probing Global Layout Reasoning in Infographic Editing

Cheng Yang, Chufan Shi, Huijuan Wang +6

Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, i…

cs.CV2026

Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination

Chufan Shi, Cheng Yang, Yaokang Wu +4

Vision-Language Models (VLMs) often produce self-reflective statements like "let me check the figure again" during reasoning. Do such statements trigger genuine visual re-examinati…

cs.SD2025

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation

Jaechul Roh, Zachary Novack, Yuefeng Peng +3

Generative AI systems for music and video commonly use text-based filters to prevent regurgitation of copyrighted material. We expose a significant vulnerability in this approach b…

cs.CR2024★ 2 cited

Imprompter: Tricking LLM Agents into Improper Tool Use

Xiaohan Fu, Shuheng Li, Zihan Wang +4

Large Language Model (LLM) Agents are an emerging computing paradigm that blends generative machine learning with tools such as code interpreters, web browsing, email, and more gen…

cs.CR2023★ 6 cited

Misusing Tools in Large Language Models With Visual Adversarial Examples

Xiaohan Fu, Zihan Wang, Shuheng Li +4

Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities. These new capabilities bring new benefits and also new security ri…

cs.CL2021

An Empirical Study of Extrapolation in Text Generation with Scalar Control

Aashi Jain, Taylor Berg-Kirkpatrick

We conduct an empirical evaluation of extrapolation performance when conditioning on scalar control inputs like desired output length, desired edit from an input sentence, and desi…