From the 2 of 6 linked papers with an AI index.
6 papers
Before Agents Speak: Pre-hoc Failure Risk Inference in Multi-Agent Systems
Shi Lin, Chenpei Wang, Peng Qian +4
The paper introduces HalluProp, a framework that predicts which agents in a large‑language‑model based multi‑agent system are likely to hallucinate and estimates the overall system…
Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions
Shi Lin, Peng Qian, Dinghao Liu +5
The paper introduces Recast, a framework that predicts safety risks in multi‑turn interactions with large language models by forecasting how risks evolve over dialogue trajectories…
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
BartÅomiej Marek, Lorenzo Rossi, Vincent Hanke +4
Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiv…
A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures
Dezhang Kong, Shi Lin, Zhenhua Xu +16
In recent years, Large-Language-Model-driven AI agents have exhibited unprecedented intelligence and adaptability. Nowadays, agents are undergoing a new round of evolution. They no…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
LLMs can be Dangerous Reasoners: Analyzing-based Jailbreak Attack on Large Language Models
Shi Lin, Hongming Yang, Rongchang Li +4
The rapid development of Large Language Models (LLMs) has brought impressive advancements across various tasks. However, despite these achievements, LLMs still pose inherent safety…