5 papers
Amulet: Fast TEE-Shielded Inference for On-Device Model Protection
Zikai Mao, Lingchen Zhao, Lei Xu +4
On-device machine learning (ML) introduces new security concerns about model privacy. Storing valuable trained ML models on user devices exposes them to potential extraction by adv…
SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats
Qingzhao Zhang, Shaocheng Luo, Z. Morley Mao +2
Autonomous vehicles, including self driving cars, ground robots, and drones, rely on multi-modal sensor pipelines for safe operation, yet remain vulnerable to adversarial sensor at…
Compute Or Load KV Cache? Why Not Both?
Shuowei Jin, Xueshen Liu, Qingzhao Zhang +1
Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-va…
Eagle: Efficient Training-Free Router for Multi-LLM Inference
Zesen Zhao, Shuowei Jin, Z. Morley Mao
The proliferation of Large Language Models (LLMs) with varying capabilities and costs has created a need for efficient model selection in AI systems. LLM routers address this need…
AutoSpec: Automated Generation of Neural Network Specifications
Shuowei Jin, Francis Y. Yan, Taobo Liao +5
The increasing adoption of neural networks in learning-augmented systems highlights the growing need for model safety and robustness, especially in safety-critical domains. While r…