most citedThe Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models

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

collaborators

5 papers

cs.CL2026

PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions

Chenxin Li, Zhengyao Fang, Zhengyang Tang +18

Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature sti…

cs.CL2026

PhoneWorld: Scaling Phone-Use Agent Environments

Zhengyang Tang, Yuxuan Liu, Xin Lai +21

A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Existing mobile-agent benchmarks…

cs.CL2026

Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents

Zhengyang Tang, Yi Zhang, Chenxin Li +18

When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent re…

cs.HC20265 cited

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models

Yike Shi, Qing Xiao, Qing Hu +2

Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as ma…

cs.HC20261 cited

SusBench: An Online Benchmark for Evaluating Dark Pattern Susceptibility of Computer-Use Agents

Longjie Guo, Chenjie Yuan, Mingyuan Zhong +7

As LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes incre…