3 citations · 9 across the 16 of their papers we have counts for
5 papers · 1 filter
Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
Pengfei He, Ash Fox, Lesly Miculicich +7
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to li…
Memory Injection Attacks on LLM Agents via Query-Only Interaction
Shen Dong, Shaochen Xu, Pengfei He +5
Agents powered by large language models (LLMs) have demonstrated strong capabilities in a wide range of complex, real-world applications. However, LLM agents with a compromised mem…
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
Shenglai Zeng, Jiankun Zhang, Bingheng Li +8
Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…
Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning
Pengfei He, Zitao Li, Yue Xing +3
Zero-shot reasoning methods with Large Language Models (LLMs) offer significant advantages including great generalization to novel tasks and reduced dependency on human-crafted exa…
Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study
Pengfei He, Yingqian Cui, Han Xu +4
In-context learning (ICL) has emerged as a powerful capability for large language models (LLMs) to adapt to downstream tasks by leveraging a few (demonstration) examples. Despite i…