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Yingshui Tan

ACL 2026×2ICLR 2026多模态安全评测与基准智能体与工具推理
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0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #5/15ACL 2026Poster· Natural Language Processing

USB : A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS

Baolin Zheng · Guanlin Chen · Qingyang Teng · Hongqiong Zhong · Yingshui Tan · Zhendong Liu · Weixun Wang · JIAHENG LIU · Jian Yang · Huiyun Jing · Jincheng Wei · Wenbo Su · Xiaoyong Zhu · Zerui Huang (Taobao &amp · Kaifu Zhang

摘要

Despite their rapid advancement, Multimodal Large Language Models (MLLMs) remain vulnerable to diverse safety risks. Current benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale, and the oversight of complex modality combinations (e.g., cross-modal risk...

安全多模态评测与基准OpenReview 项目页
作者位次 #4/9ACL 2026Findings· Findings of ACL

B ackdoor A gent: A Unified Framework for Backdoor Attacks on LLM -based Agents

Yunhao Feng · Yige Li · Yutao Wu · Yingshui Tan · Yanming Guo · Yifan Ding · Kun Zhai · Xingjun Ma · Yu-Gang Jiang

摘要

Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use. While this design enables autonomy, it also expands the attack surface for backdoor threats. Backdoor triggers injected into specific stages of an agent workflow can persist t...

智能体与工具安全推理OpenReview 项目页

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