activity
20242026
collaborators

33 papers

cs.SE2026

AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming

Pingchuan Ma, Zhaoyu Wang, Zimo Ji +5

Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments. However, their autonomy poses significant…

cs.CR2026

Cloak and Detonate: Scanner Evasion and Dynamic Detection of Agent Skill Malware

Zimo Ji, Congying Xu, Zongjie Li +4

LLM coding agents increasingly rely on third-party agent skills from public marketplaces, which execute with the agent's privileges and create a software supply-chain attack surfac…

cs.SE2026

Coding Agents Are Guessing: Measuring Action-Boundary Violations in Underspecified DevOps Instructions

Zimo Ji, Zekai Zhang, Congying Xu +4

LLM coding agents are increasingly deployed to act autonomously on real production infrastructure. They execute shell commands, modify repositories, and call operational APIs. Howe…

cs.CR2026

Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment

Yuchong Xie, Mingyu Luo, Zesen Liu +7

Coding agents powered by large language models are becoming central modules of modern IDEs, helping users perform complex tasks by invoking tools. While powerful, tool invocation o…

cs.SE2026

SkillReducer: Optimizing LLM Agent Skills for Token Efficiency

Yudong Gao, Zongjie Li, Yuanyuan Yuan +3

LLM-based coding agents rely on \emph{skills}, pre-packaged instruction sets that extend agent capabilities, yet every token of skill content injected into the context window incur…

cs.AI2026

Unlocking Proactivity in Task-Oriented Dialogue

Azure Zhang, Ning Gao, Yuqin Dai +7

Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance wi…