17 citations · 29 across the 9 of their papers we have counts for
11 papers
Better Nearest Neighbor Graph Indices via (Efficient) LLM-Guided Pruning
Fangzhou Wu, Haike Xu, Sandeep Silwal
Graph-based approximate nearest neighbor search (ANNS) is widely used for large-scale semantic search. Its indices are constructed primarily based on geometric relationships among…
DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration
Yanxing Guo, Zihao Zheng, Fangzhou Wu +4
Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposition through structured pipelin…
When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
Yuan Tian, Bing Hu, Fang Wu +3
Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly understood. Existing systems already…
Randomization Boosts KV Caching, Learning Balances Query Load: A Joint Perspective
Fangzhou Wu, Sandeep Silwal, Qiuyi +1
KV caching is a fundamental technique for accelerating Large Language Model (LLM) inference by reusing key-value (KV) pairs from previous queries, but its effectiveness under limit…
Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
Fangzhou Wu, Sandeep Silwal
Increasing demand for Large Language Models (LLMs) services imposes substantial deployment and computation costs on providers. LLM routing offers a cost-efficient solution by direc…
FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
Jiongxiao Wang, Fangzhou Wu, Wendi Li +5
Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external informa…
System-Level Defense against Indirect Prompt Injection Attacks: An Information Flow Control Perspective
Fangzhou Wu, Ethan Cecchetti, Chaowei Xiao
Large Language Model-based systems (LLM systems) are information and query processing systems that use LLMs to plan operations from natural-language prompts and feed the output of…
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
Fangzhou Wu, Ning Zhang, Somesh Jha +2
Large Language Model (LLM) systems are inherently compositional, with individual LLM serving as the core foundation with additional layers of objects such as plugins, sandbox, and…
WIPI: A New Web Threat for LLM-Driven Web Agents
Fangzhou Wu, Shutong Wu, Yulong Cao +1
With the fast development of large language models (LLMs), LLM-driven Web Agents (Web Agents for short) have obtained tons of attention due to their superior capability where LLMs…
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
Fangzhou Wu, Xiaogeng Liu, Chaowei Xiao
With the advancement of Large Language Models (LLMs), significant progress has been made in code generation, enabling LLMs to transform natural language into programming code. Thes…
Exploring the Limits of ChatGPT in Software Security Applications
Fangzhou Wu, Qingzhao Zhang, Ati Priya Bajaj +4
Large language models (LLMs) have undergone rapid evolution and achieved remarkable results in recent times. OpenAI's ChatGPT, backed by GPT-3.5 or GPT-4, has gained instant popula…