7 papers
Blocking at the Boundary: Auditing Long-Horizon Agents against Staged Prompt Injection
Jingkai Liu, Yufei Han, Xiaoting Lyu +2
Long-horizon agents consume external content, invoke tools, and modify persistent state. Indirect prompt injection can exploit task-specific context, propagate across causally conn…
LLM-Assisted Automatic Security Proofs for Cryptographic Protocols: How Far Are We?
Tianjian Liu, Shicheng Feng, Jin'ao Shang +5
Large language models (LLMs) have shown strong potential for assisting software and security analysis tasks, yet their effectiveness in cryptographic symbolic protocol verification…
The Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactions
Xiaoting Lyu, Xinbo Ma, Yufei Han +4
Large language model (LLM)-based scientific agents are increasingly used for scientific problem solving, yet their robustness to imperfections arising during multi-turn interaction…
AgentLeak: Cloning Stronger LLM Agent Capabilities onto Weaker Agents Beyond Skill Stealing
Xiaoting Lyu, Yuhong Wu, Yufei Han +5
Large language model (LLM) agents increasingly achieve long-horizon tasks by combining foundation models with explicit skills and implicit procedural knowledge acquired through exe…
BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts
Xiaoting Lyu, Yufei Han, Hangwei Qian +6
Recent knowledge graph (KG)-enhanced large language models (LLMs) move beyond purely textual knowledge augmentation by encoding retrieved subgraphs into continuous soft prompts via…
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
Xiaoting Lyu, Yufei Han, Wei Wang +5
Federated Learning (FL) is a collaborative machine learning technique where multiple clients work together with a central server to train a global model without sharing their priva…
Cross-Context Backdoor Attacks against Graph Prompt Learning
Xiaoting Lyu, Yufei Han, Wei Wang +3
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learn…