1 citations · 2 across the 20 of their papers we have counts for
7 papers · 1 filter
HybridFlow: Resource-Adaptive Subtask Routing for Efficient Edge-Cloud LLM Inference
Jiangwen Dong, Jiayu Li, Tianhang Zheng +1
Edge-cloud collaborative inference is becoming a practical necessity for LLM-powered edge devices: on-device models often cannot afford the required reasoning capability, while clo…
Enhancing BGP Security by Understanding BGP's Language with LLMs
Heng Zhao, Ruoyu Wang, Tianhang Zheng +4
The trust-based nature of Border Gateway Protocol (BGP) makes it vulnerable to prefix hijacking and misconfigurations. Traditional BGP anomaly detection relies on manual inspection…
NonTextual Target Attack
Xinzhe Huang, Wenjing Hu, Tianhang Zheng +6
Existing gradient-based jailbreak attacks on Large Language Models (LLMs) typically optimize adversarial suffixes to align the LLM output with predefined target responses. However,…
Dynamic Jailbreaking Attack
Kedong Xiu, Yunhan Yang, Churui Zeng +6
Existing gradient-based jailbreak attacks typically optimize a fixed-length adversarial suffix toward a predefined target response with a static optimization strategy. However, thi…
HarmMetric Eval: Benchmarking Metrics and Judges for LLM Harmfulness Assessment
Langqi Yang, Tianhang Zheng, Yixuan Chen +6
The potential of large language models (LLMs) to generate harmful content poses a significant safety risk for data management, as LLMs are increasingly being used as engines for da…
MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies
Weiwei Qi, Shuo Shao, Wei Gu +4
Large Language Models (LLMs) have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating…
DualBreach: Efficient Dual-Jailbreaking via Target-Driven Initialization and Multi-Target Optimization
Xinzhe Huang, Kedong Xiu, Tianhang Zheng +5
Recent research has focused on exploring the vulnerabilities of Large Language Models (LLMs), aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insuf…