6 papers · 1 filter
Purpose IR: Compiling User Purpose for Privacy-Preserving Agent Workflow State
Dongsheng Chen, Xiangyu Zhao, Lei Ma +2
LLM-agent workflows routinely transform rich observations into plans, summaries, memories, handoffs, and other reusable state. This creates a privacy boundary that task relevance a…
SaplingGuard: A Multidimensional-Profile-Aware Multi-Agent Guardrail for Developmentally Safe Adolescent-LLM Interaction
Jing Tan, Yifan Liu, Yi Lin +6
As adolescents increasingly use LLMs in everyday life, ensuring safe and developmentally appropriate responses has become essential. However, existing LLM guardrails primarily targ…
SameFact: The Same Safety Facts Lead to Different Responses Across Interfaces
Dongsheng Chen, Jiaxin Zhang, Lei Ma +2
Safety evaluations often ask whether a model recognizes that an action is unsafe, whereas agent evaluations ask what the model chooses to do. Using safety judgments as evidence abo…
Says Block, Still Acts: Why LLM Safety Judgments Fail to Govern Action in LLM Agents
Dongsheng Chen, Xiangyu Zhao, Xin Yao +1
Large language model agents can correctly judge that an action should be blocked while still preferring to take it. We ask why this judgment-action disconnect arises, and whether e…
"Allow" to Achieve, Over-Privileged Inadvertently: The Unintended Cost of Task-Completion-Driven Pop-up Decisions in Mobile GUI Agents
Dongsheng Chen, Yuxuan Li, Guanhua Chen +5
Mobile GUI agents routinely encounter system permission dialogs during task execution, yet their ability to grant only permissions that are necessary for the delegated task remains…
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters
Yan Yang, Zeguan Xiao, Xin Lu +5
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release…